The hair follicle (HF) is a cyclic biological system that progresses through stages of growth, regression and quiescence, each being characterized by unique patterns of gene activation and silencing. MicroRNAs (miRNAs) are critically important for gene silencing and delineating their role in hair cycle may provide new insights into mechanisms of hair growth control and epithelial tissue remodelling. The aims of this study were: 1) To define changes in the miRNA profiles in skin during hair cycle-associated tissue remodelling; 2) To determine the role of individual miRNAs in regulating gene expression programs that drive HF growth, involution and quiescence; 3) and to explore the role of miRNAs in mediating the effects of BMP signalling in the skin. To address Aims 1 & 2, global miRNA expression profiling in the skin was performed and revealed marked changes in miRNAs expression during distinct stages of the murine hair cycle. Specifically, miR-31 markedly increased during anagen and decreased during catagen and telogen. Administration of antisense miR-31 inhibitor into mouse skin during the early- and mid-anagen phases of the hair cycle resulted in accelerated anagen development, and altered differentiation of hair matrix keratinocytes and hair shaft formation. Microarray, qRT-PCR and Western blot analyses revealed that miR-31 negatively regulates expression of Fgf10, the components of Wnt and BMP signalling pathways Sclerostin and BAMBI, and Dlx3 transcription factor, as well as selected keratin genes. Luciferase reporter assay revealed that Krt16, Krt17, Dlx3, and Fgf10 serve as direct miR-31 targets. In addition, miR-214 was identified as a potent inhibitor of the Wnt signalling pathway in the keratinocytes. Mutually exclusive expression patterns of miR-214 and β-catenin was observed during HF morphogenesis. MiR-214 decreases the expression of β-catenin and other components of Wnt signalling pathways c-myc, cyclin D1, and Pten in the keratinocytes. Luciferase reporter assay proved that β-catenin serves as a direct target of miR-214. In addition, miR-214 prevented translocation of β-catenin into the nucleus in response to the treatment with an activator of the Wnt signalling pathway lithium chloride, and abrogated the lithium-induced increase of the expression of the Wnt target gene VI Axin2. This suggests that miR-214 may indeed be involved in regulation of skin development and regeneration at least in part, by controlling the expression of β-catenin and the activity of the Wnt signalling pathway. To address Aim 3, the role of miRNAs in mediating the effects of the bone morphogenetic protein (BMP) signalling in the skin was explored. MiRNAs were isolated from the primary mouse keratinocytes treated with BMP4 and processed for analysis of global miRNA expression using the microarray approach. Microarray and real-time PCR analysis revealed BMP4-dependent changes in the expression of distinct miRNAs, including miR-21, which expression was strongly decreased in the keratinocytes after BMP4 treatment. In contrast, miR-21 expression was substantially higher in the skin of transgenic mice over-expressing BMP antagonist Noggin. Transfection of the keratinocytes with miR-21 mimic revealed existence of two groups of the BMP target genes, which are differentially regulated by miR-21. Thus, this suggests a novel mechanism controlling the effects of BMP signalling in the keratinocytes. Thus, miRNAs play important roles in regulating gene expression programs in the skin during hair cycle. By targeting a number of growth regulatory molecules, transcription factors and cytoskeletal proteins, miRNAs are involved in establishing an optimal balance of gene expression in the keratinocytes required for the HF and skin homeostasis.

MicroRNAs (miRNAs) are a diverse and evolutionarily conserved class of non-coding RNAs that play a multitude of roles in many branches of eukaryotic biology. The regulation of miRNAs is dynamically controlled both spatially and temporally, and the expression of miRNAs can be modulated at the level of transcription or at points downstream of the miRNA maturation process. A relevant example of post-transcriptional miRNA regulation is the blockade of let-7 precursor miRNAs by Lin28 in embryonic stem cells. This pathway, which is initiated by the small RNA-binding protein Lin28, recruits the terminal uridyl transferase (TUTase) Zcchc11 to add a non-templated oligouridine tail to the miRNAs 3' end, and signals it for degradation by the cytoplasmic exonuclease Dis3l2. The Lin28/let-7 axis is essential for development and metabolic homeostasis, and is reactivated in a subset of human cancers. This thesis describes the biochemical mechanism underlying Lin28-mediated degradation of let-7, as well as a novel role for Zcchc11 and the related TUTase Zcchc6 in targeting mature developmental miRNAs in a Lin28-independent manner.

Cervical cancer is the third most common cancer among women worldwide, and the fourth leading cause of cancer mortality. Despite significant declines in the incidence and mortality rates of cervical cancer in Canada, it remains the 4th most common cancer in women aged 20-29 years. In order to gain novel insights into cervical cancer tumourigenesis and clinical outcome, we investigated and characterized the alterations in microRNA (miRNA) expression in this disease. Firstly, we performed global miRNA expression profiling of cervical cancer cell lines (n=3), and patient specimens (n=79). From this analysis, we identified miR-196b to be significantly down-regulated in cervical cancer, and characterized its role in regulating the HOXB7~VEGF axis. The global miRNA expression data also led to the development of a candidate 9-miRNA signature that was prognostic for disease-free survival in patients with cervical cancer, although we were unable to validate this signature in an independent cohort. This report describes important considerations concerning the development and validation of microRNA signatures for cervical cancer. Our investigations also led us to a comparison of three methods for measuring miRNA abundance: the TaqMan Low Density Array, the NanoString nCounter assay, and single-well quantitative real-time PCR. Our findings demonstrated limited concordance between the TLDA and NanoString platforms, although each platform correlated well with PCR, which is considered the gold standard for nucleic acid quantification. Furthermore, we examined biases created by amplification protocols for microarray studies. Our analysis demonstrated that performing a correction using the LTR-method (linear transformation of replicates) could help mitigate, but not completely eliminate such biases. Overall, this report presents insights into the role of miRNAs in cervical cancer, as well as an evaluation of technical considerations concerning miRNA and mRNA expression profiling studies.

The transcriptome of the mammalian brain is extensively modified by adenosine to inosine (A-to-I) nucleotide conversion by two adenosine deaminases (ADAR1 and ADAR2). As adenosine and inosine have different base pairing properties, A-to-I RNA editing shapes the functional output of both coding and non-coding RNAs (ncRNAs) in the brain. The aim of this thesis was to identify editing events in small regulatory ncRNAs (miRNAs) and to determine their temporal and spatial editing status in the developing and adult mouse brain. To do this, we initially analyzed the editing status of miRNAs from different developmental time points of the mouse brain. We detected novel miRNA substrates subjected to A-to-I editing and found a general increase in miRNA editing during brain development, implicating a more stringent control of miRNAs as the brain matures. Most of the edited miRNAs were found to be transcribed as a single long consecutive transcript from a large gene cluster. However, maturation from this primary miRNA (pri-miRNA) transcript into functional forms of miRNAs is regulated individually, and might be influenced by the ADAR proteins in an editing independent matter. We also found that edited miRNAs were highly expressed at the synapse, implicating a role as local regulators of synaptic translation. We further show that the increase in editing during development is explained by a gradual accumulation of the ADAR enzymes in the nucleus. Specifically for ADAR2, we found a developmentally increasing interaction with two factors, importin-α4 and Pin1, that facilitate nuclear localization of the editing enzyme. We have also found that selectively edited stem loops often are flanked by other long stem loop structures that induce editing in cis. This may explain why multiple pri-miRNAs are edited within the same cluster. In conclusion, this thesis has significantly increased the understanding of the dynamics of both editing substrates and enzymes in the developing and mature brain. / <p>At the time of the doctoral defense, the following paper was unpublished and had a status as follows: Paper 2: Manuscript.</p>

miRNAs are critical modulators in the development and progression of cancer. Emerging evidence suggests that they are drivers of ovarian cancer. A better understanding of the molecular underpinnings of the development, progression and chemoresistance of the disease is critical for the development of new, more effective therapies. Here we explore the expression patterns of miRNAs as they relate to gene expression, as they differ across molecular subtypes of the disease. We examine the correlation structure of miRNA expression with mRNA expression in two distinct genomic datasets and report on patterns in correlation structure in several subsets of the data. We find that the datasets show consistency in their correlation structure, and in the specific miRNA-mRNA pairs that are either highly positively or negatively correlated. The data include a larger number of strong positive and strong negative correlations than would be expected by chance, indicating that biological relationships between the types of data are detectable in these datasets. We further find an enrichment for positively-correlated miRNA-mRNA pairs in which the miRNA is encoded in close proximity to the mRNA. The correlation of miRNA and mRNA is apparently unaffected by miRNA and mRNA expression level; similarly the two molecular subtypes do not contain differences in their correlation. We find that the recently described poorer prognosis, or angiogenic, subtype has a generally lower miRNA activity than the second, non-angiogenic, subtype. The subtypes are characterized by a consistent pattern of differential miRNA expression. We also report on a switch-like relationship between the expression levels of certain miRNAs and the genes that are anticorrelated with them. We propose these miRNAs drive many of the differences in the subtypes both directly, by RISC-mediated repression of target messages and indirectly, by repressing transcription factors that regulate expression in the cell. We build models of patient survival and time-to-relapse based on these miRNA expression data and inferred miRNA activity scores, using several types of univariate and variable selection models. We find essentially no survival-predictive information provided by the RE score data. While the direct miRNA expression measurements may contain some predictive power, we find that a larger dataset and the segretation of that dataset into distinct molecular phenotypes is likely to be necessary to produce a useful model of survival in ovarian cancer.

No description available.

MicroRNAs (miRNAs) are a family of small non-coding RNAs that regulate gene expression at both the mRNA and protein levels. MiRNAs have been shown to affect neuronal differentiation, synaptosomal complex localization and synapse plasticity, all functions thought to be disrupted in schizophrenia. We investigated the expression of 667 miRNAs (miRBase v.13) in the prefrontal cortex of individuals with schizophrenia (SZ, N = 35) and bipolar disorder (BP, N =35) using a real-time PCR-based Taqman Low Density Array (TLDA). After extensive QC steps, 441 miRNAs were included in the final analyses. At a FDR of 10%, 22 miRNAs were identified as differentially expressed between cases and controls, 7 dysregulated in SZ and 15 in BP. Using in silico target gene prediction programs, the 22miRNAs were found to target brain-specific genes contained within networks overrepresented for neurodevelopment, behavior, and SZ and BP disease development. Given that miRNAs can bind to their targets with imperfect complementarity, computational prediction of true miRNA:mRNA interactions has been difficult and therefore, functional validation of miRNA:mRNA interactions has been relatively sparse. Thus, it was the goal of this study to demonstrate biological functionality of miRNAs on their targets by evaluating transcriptional and translational levels of gene expression(real-time PCR, western blot) as well as determining miRNA target-site specificity (luciferase reporter gene assays). We investigated two miRNAs, miR-132 and miR-137, both of which have been shown to regulate neuronal function and development, and are believed to be associated with schizophrenia from two distinct avenues of research, miR-132 from expression studies and miR-137 from genetic studies. We demonstrated miR-132 down-regulates NTF3, DISC1, and GRIK5 at the transcript level and down-regulates GRIK5 at the protein level as well. Furthermore, we demonstrated miR-137 down-regulates TCF4, CACNA1C, CDK6, ANK3, and ZNF804A at the transcript level, and down-regulates TCF4, CACNA1C, and CDK6 at the protein level. Going further, we also demonstrated miR-137 binds specifically to target sites in the 3'-UTR of CACNA1C, TCF4, and CDK6, suggesting repression of these genes is directly mediated by miR-137. In total, this study provides strong evidence that miRNA dysregulation may contribute to schizophrenia pathogenesis.

The aim of the study was to identify molecular markers that can determine progression of low grade glioma. This was done using various approaches such as IDH1 and IDH2 mutation analysis, MGMT methylation analysis, copy number analysis using array comparative genomic hybridisation and identification of differentially expressed miRNAs using miRNA microarray analysis. IDH1 mutation was present at a frequency of 71% in low grade glioma and was identified as an independent marker for improved OS in a multivariate analysis, which confirms the previous findings in low grade glioma studies. IDH1 mutation was associated with MGMT promoter methylation when partially methylated tumours were grouped with methylated tumours. Grade II and grade III tumour comparison analysis revealed 14 novel significant miRNAs with differential expression. A miRNA signature was shown for histological subtypes, oligoastrocytoma and anaplastic oligoastrocytoma, following the miRNA expression analysis in grade II and grade III tumors based on histology. Oligoastrocytoma presented a more similar profile to oligodendroglioma, but anaplastic oligoastrocytoma was more similar to anaplastic astrocytoma. Five novel miRNAs were identified in grade III tumours, when comparing IDH1 mutant and IDH1 wild type tumours. Analysis of paired samples of primary/recurrent tumours revealed that additional genomic changes may promote tumour progression. For each of the pair, the two samples were genomically different and in each case, the reccurent tumours had more copy number aberrations than the corresponding primary tumours. Cell cultures derived from the tumour biopsies were not representative of the low grade glioma in vivo, which was evident from the differences identified in the miRNA expression and copy number changes in the paired samples. IDH1 mutation present in tumour biopsies was not maintained in their respective cell cultures. These findings give an insight into the molecular mechanisms involved in the tumourigenesis of low grade glioma and also tumour progression.

<p>CD4+ T cells play a crucial in the adaptive immune system. They function as the central hub to orchestrate the rest of immunity: CD4+ T cells are essential governing machinery in antibacterial and antiviral responses by facilitating B cell affinity maturation and coordinating the innate and adaptive immune systems to boost the overall immune outcome; on the contrary, hyperactivation of the inflammatory lineages of CD4+ T cells, as well as the impairments of suppressive CD4+ regulatory T cells, are the etiology of various autoimmunity and inflammatory diseases. The broad role of CD4+ T cells in both physiological and pathological contexts prompted me to explore the modulation of CD4+ T cells on the molecular level.</p><p>microRNAs (miRNAs) are small RNA molecules capable of regulating gene expression post-transcriptionally. miRNAs have been shown to exert substantial regulatory effects on CD4+ T cell activation, differentiation and helper function. Specifically, my lab has previously established the function of the miR-17-92 cluster in Th1 differentiation and anti-tumor responses. Here, I further analyzed the role of this miRNA cluster in Th17 differentiation, specifically, in the context of autoimmune diseases. Using both gain- and loss-of-function approaches, I demonstrated that miRNAs in miR-17-92, specifically, miR-17 and miR-19b in this cluster, is a crucial promoter of Th17 differentiation. Consequently, loss of miR-17-92 expression in T cells mitigated the progression of experimental autoimmune encephalomyelitis and T cell-induced colitis. In combination with my previous data, the molecular dissection of this cluster establishes that miR-19b and miR-17 play a comprehensive role in promoting multiple aspects of inflammatory T cell responses, which underscore them as potential targets for oligonucleotide-based therapy in treating autoimmune diseases. </p><p>To systematically study miRNA regulation in effector CD4+ T cells, I devised a large-scale miRNAome profiling to track in vivo miRNA changes in antigen-specific CD4+ T cells activated by Listeria challenge. From this screening, I identified that miR-23a expression tightly correlates with CD4+ effector expansion. Ectopic expression and genetic deletion strategies validated that miR-23a was required for antigen-stimulated effector CD4+ T cell survival in vitro and in vivo. I further determined that miR-23a targets Ppif, a gatekeeper of mitochondrial reactive oxygen species (ROS) release that protects CD4+ T cells from necrosis. Necrosis is a type of cell death that provokes inflammation, and it is prominently triggered by ROS release and its consequent oxidative stress. My finding that miR-23a curbs ROS-mediated necrosis highlights the essential role of this miRNA in maintaining immune homeostasis. </p><p>A key feature of miRNAs is their ability to modulate different biological aspects in different cell populations. Previously, my lab found that miR-23a potently suppresses CD8+ T cell cytotoxicity by restricting BLIMP1 expression. Since BLIMP1 has been found to inhibit T follicular helper (Tfh) differentiation by antagonizing the master transcription factor BCL6, I investigated whether miR-23a is also involved in Tfh differentiation. However, I found that miR-23a does not target BLIMP1 in CD4+ T cells and loss of miR-23a even fostered Tfh differentiation. This data indicate that miR-23a may target other pathways in CD4+ T cells regarding the Tfh differentiation pathway.</p><p>Although the lineage identity and regulatory networks for Tfh cells have been defined, the differentiation path of Tfh cells remains elusive. Two models have been proposed to explain the differentiation process of Tfh cells: in the parallel differentiation model, the Tfh lineage is segregated from other effector lineages at the early stage of antigen activation; alternatively, the sequential differentiation model suggests that naïve CD4+ T cells first differentiate into various effector lineages, then further program into Tfh cells. To address this question, I developed a novel in vitro co-culture system that employed antigen-specific CD4+ T cells, naïve B cells presenting cognate T cell antigen and BAFF-producing feeder cells to mimic germinal center. Using this system, I were able to robustly generate GC-like B cells. Notably, well-differentiated Th1 or Th2 effector cells also quickly acquired Tfh phenotype and function during in vitro co-culture, which suggested a sequential differentiation path for Tfh cells. To examine this path in vivo, under conditions of classical Th1- or Th2-type immunizations, I employed a TCRβ repertoire sequencing technique to track the clonotype origin of Tfh cells. Under both Th1- and Th2- immunization conditions, I observed profound repertoire overlaps between the Teff and Tfh populations, which strongly supports the proposed sequential differentiation model. Therefore, my studies establish a new platform to conveniently study Tfh-GC B cell interactions and provide insights into Tfh differentiation processes.</p> / Dissertation

RNA Sequencing has recently emerged as a breakthrough technology for microRNA (miRNA) discovery. This technology has allowed the discovery of thousands of miRNAs in a large number of species. However, despite the benefits of this technology, it also carries its own limitations, including the need for sequencing read libraries and of the genome. Differently, ab initio computational methods need only the genome as input to search for genonic locus likely to give rise to novel miRNAs. In the core of most of these methods, there are predictive models induced by using data mining techniques able to distinguish between real (positive) and pseudo (negative) miRNA precursors (pre-miRNA). Nevertheless, the applicability of current literature ab initio methods have been compromised by high false detection rates and/or by other computational difficulties. In this work, we investigated how the main aspects involved in the induction of predictive models for pre-miRNA affect the predictive performance. Particularly, we evaluate the discriminant power of feature sets proposed in the literature, whose computational costs and composition vary widely. The computational experiments were carried out using sequence data from 45 species, which covered species from eight phyla. The predictive performance of the classification models induced using large training set sizes (&ge; 1; 608) composed of instances extracted from real and pseudo human pre-miRNA sequences did not differ significantly among the feature sets that lead to the maximal accuracies. Moreover, the differences in the predictive performances obtained by these models, due to the learning algorithms, were neglectable. Inspired by these results, we obtained a feature set which can be computed 34 times faster than the less costly among those feature sets, producing the maximal accuracies, albeit the proposed feature set has achieved accuracy within 0.1% of the maximal accuracies. When classification models using the elements previously discussed were induced using small training sets (120) from 45 species, we showed that the feature sets that produced the highest accuracies in the classification of human sequences were also more likely to produce higher accuracies for other species. Nevertheless, we showed that the learning complexity of pre-miRNAs vary strongly among species, even among those from the same phylum. These results showed that the existence of specie specific features indicated in previous studies may be correlated with the learning complexity. As a consequence, the predictive accuracies of models induced with different species and same features and instances spaces vary largely. In our results, we show that the use of training examples from species phylogenetically more complex may increase the predictive performances for less complex species. Finally, by using ensembles of computationally less costly feature sets, we showed alternative ways to increase the predictive performance for many species while keeping the computational costs of the analysis lower than those using the feature sets from the literature. Since in miRNA discovery the number of putative miRNA loci is in the order of millions, the analysis of putative miRNAs using a computationally expensive feature set and or inaccurate models would be wasteful or even unfeasible for large genomes. In this work, we explore most of the learning aspects implemented in current ab initio pre-miRNA prediction tools, which may lead to the development of new efficient ab initio pre-miRNA discovery tools / O sequenciamento de pequenos RNAs surgiu recentemente como uma tecnologia inovadora na descoberta de microRNAs (miRNA). Essa tecnologia tem facilitado a descoberta de milhares de miRNAs em um grande número de espécies. No entanto, apesar dos benefícios dessa tecnologia, ela apresenta desafios, como a necessidade de construir uma biblioteca de pequenos RNAs, além do genoma. Diferentemente, métodos computacionais ab initio buscam diretamente no genoma regiões prováveis de conter miRNAs. A maioria desses métodos usam modelos preditivos capazes de distinguir entre os verdadeiros (positivos) e pseudo precursores de miRNA - pre-miRNA - (negativos), os quais são induzidos utilizando técnicas de mineração de dados. No entanto, a aplicabilidade de métodos ab initio da literatura atual é limitada pelas altas taxas de falsos positivos e/ou por outras dificuldades computacionais, como o elevado tempo necessário para calcular um conjunto de atributos. Neste trabalho, investigamos como os principais aspectos envolvidos na indução de modelos preditivos de pre-miRNA afetam o desempenho preditivo. Particularmente, avaliamos a capacidade discriminatória de conjuntos de atributos propostos na literatura, cujos custos computacionais e a composição variam amplamente. Os experimentos computacionais foram realizados utilizando dados de sequências positivas e negativas de 45 espécies, cobrindo espécies de oito filos. Os resultados mostraram que o desempenho preditivo de classificadores induzidos utilizando conjuntos de treinamento com 1608 ou mais vetores de atributos calculados de sequências humanas não diferiram significativamente, entre os conjuntos de atributos que produziram as maiores acurácias. Além disso, as diferenças entre os desempenhos preditivos de classificadores induzidos por diferentes algoritmos de aprendizado, utilizando um mesmo conjunto de atributos, foram pequenas ou não significantes. Esses resultados inspiraram a obtenção de um conjunto de atributos menor e que pode ser calculado até 34 vezes mais rapidamente do que o conjunto de atributos menos custoso produzindo máxima acurácia, embora a acurácia produzida pelo conjunto proposto não difere em mais de 0.1% das acurácias máximas. Quando esses experimentos foram executados utilizando vetores de atributos calculados de sequências de outras 44 espécies, os resultados mostraram que os conjuntos de atributos que produziram modelos com as maiores acurácias utilizando vetores calculados de sequências humanas também produziram as maiores acurácias quando pequenos conjuntos de treinamento (120) calculados de exemplos de outras espécies foram utilizadas. No entanto, a análise destes modelos mostrou que a complexidade de aprendizado varia amplamente entre as espécies, mesmo entre aquelas pertencentes a um mesmo filo. Esses resultados mostram que a existência de características espécificas em pre-miRNAs de certas espécies sugerida em estudos anteriores pode estar correlacionada com a complexidade de aprendizado. Consequentemente, a acurácia de modelos induzidos utilizando um mesmo conjunto de atributos e um mesmo algoritmo de aprendizado varia amplamente entre as espécies. i Os resultados também mostraram que o uso de exemplos de espécies filogeneticamente mais complexas pode aumentar o desempenho preditivo de espécies menos complexas. Por último, experimentos computacionais utilizando técnicas de ensemble mostraram estratégias alternativas para o desenvolvimento de novos modelos para predição de pre-miRNA com maior probabilidade de obter maior desempenho preditivo do que estratégias atuais, embora o custo computacional dos atributos seja inferior. Uma vez que a descoberta de miRNAs envolve a análise de milhares de regiões genômicas, a aplicação prática de modelos preditivos de baixa acurácia e/ou que dependem de atributos computacionalmente custosos pode ser inviável em análises de grandes genomas. Neste trabalho, apresentamos e discutimos os resultados de experimentos computacionais investigando o potencial de diversas estratégias utilizadas na indução de modelos preditivos para predição ab initio de pre-miRNAs, que podem levar ao desenvolvimento de ferramentas ab initio de maior aplicabilidade prática