O Diabetes Mellitus é uma das maiores causas de morte no mundo. O desenvolvimento do Diabetes Mellitus tipo 2 (DM2) está relacionado com uma série de fatores genéticos e ambientais, culminando com o desenvolvimento do DM2. Já a hiperglicemia, característica marcante da doença, está associada a uma série de complicações metabólicas e comorbidades. No entanto, nào se sabe a influência de um controle apropriado da doença, com menores níveis glicêmicos. No presente trabalho, foi utilizada a técnica de microarrays para comparar os perfis transcricionais (mRNA e microRNA) de células mononucleares de sangue periférico (PBMCs) em três grupos distintos: um grupo de pacientes DM2 descompensados (DM2-D, n=13); um grupo de pacientes DM2 compensados (DM2-C, n=14), e um grupo controle (n=10). Os dados foram analisados por meio de duas linguagens de programação: R e PERL. Após a extração dos dados utilizando-se o software Feature Extraction, versão 10.7 (Agilent Techonologies), foram realizadas correção do background, exclusão dos outliers, normalização dos dados pelo método quantile e, por fim, o ajuste de variações nãobiológicas. Os dados foram então submetidos a análise estatística rank products, sendo identificados 415 mRNAs diferencialmente expressos no grupo DM2-C relativamente aos controles, 285 no grupo DM2-D em comparação aos controles e 478 em pacientes DM2-D comparados aos DM2-C. Posteriormente, os genes diferencialmente expressos foram submetidos à analise de enriquecimento funcional (DAVID). Foram encontrados 22 e 56 termos biológicos enriquecidos (p-corrigido Benjamini-Hochberg < 0,05) para as comparações DM2-C versus controle e pacientes DM2-D versus DM2-C, respectivamente. Em ambas as comparações, um processo biológico foi considerado de interesse para o presente trabalho: resposta inflamatória. Na análise por GSEA e GSA, foram identificados 110 grupos gênicos diferencialmente expressos na comparação DM2-C versus controle. Já para a comparação DM2-D versus controles foram encontrados 297 grupos gênicos diferencialmente expressos, enquanto que na comparação DM2-D versus DM2-C, 161 grupos gênicos diferencialmente expressos. Dentre os grupos gênicos diferencialmente expressos, três merecem destaque: regulação do reparo do DNA (GO: 0006282), resposta ao superóxido (GO: 0000303) e resposta ao estresse do retículo endoplasmático (GO: 0034976). Ainda, 97 microRNAs foram diferencialmente expressos na comparação DM2-C versus controles, 54 na comparação DM2-D versus controles e 101 na comparação DM2-D versus DM2-C. Assim, diferentes grupos gênicos provavelmente foram modulados pela hiperglicemia, além de terem sido descobertos novos microRNAs relacionados a altos níveis de glicose. / Diabetes mellitus is a major cause of death worldwide. The development of type 2 Diabetes Mellitus (T2D) is associated with a number of genetic and environmental factors, culminating in the development of T2D. Hyperglycemia, a hallmark of the disease, is associated with a number of metabolic complications and comorbidities. However, the influence of a proper control of the disease, with lower glucose levels is unknown. In this study, we used the microarrays technique to compare the transcriptional profiles (mRNA and microRNA) of peripheral blood mononuclear cells (PBMCs) in three distinct groups: a group of patients with uncontrolled T2D patients (T2D-U, n = 13) a group of controlled T2D patients (T2D-C, n = 14) and control group (n = 10). Data were analyzed using two programming languages: R and PERL. After extracting the data using the Feature Extraction software, version 10.7 (Agilent Technologies), background correction, outliers exclusion, data normalization by quantile and adjustmesnt of non-biological variations were performed. The data were then statistically analyzed by the rank products test, which identified 415 differentially expressed mRNAs in T2D-C group compared to controls, 285 in group T2D-U in comparison with controls and 478 when T2D-U and T2D-C are compared. Thereafter, the differentially expressed genes were subjected to functional enrichment analysis (DAVID). 22 and 56 biologically enriched terms were found (Benjamini-Hochberg-corrected p value<0.05), when comparing T2D-C with controls and T2D-U with T2D-C, respectively. In both comparisons, inflammatory response was selected as a biological process of interest. The analysis by GSEA and GSA identified 110 differentially expressed gene sets in comparison T2D-C versus control. As for the comparison T2D-U versus control, 297 gene sets were found differentially expressed, whereas in comparison T2D-U versus T2D-C, 161 differentially expressed gene sets were found. Among the differentially expressed gene sets, three stand out: regulation of DNA repair (GO: 0006282), superoxide response (GO: 0000303) and response to endoplasmic reticulum stress (GO: 0034976). Still, 97 microRNAs were differentially expressed in the T2D-C versus controls comparison, 54 when comparing T2D-U versus controls and 101 in the comparison of T2D-U versus T2D-C. Thus, different gene sets were probably modulated by hyperglycemia, and new microRNAs related to high levels of glucose were discovered.

A atividade física aeróbia reduz a incidência de diversos tipos de câncer e atenua o crescimento tumoral. Entretanto, existe uma lacuna na literatura sobre os mecanismos envolvidos nessa resposta. Dados recentes demonstram a importância da manutenção da função da resposta imune antitumoral para a atenuação da progressão da doença e estratégias capazes de aumentar essa resposta permanecem como um grande desafio. Diante disso, delineamos um estudo experimental para investigar especificamente a possível contribuição da atividade física aeróbia para a resposta imune antitumoral. Na presente dissertação, nós demonstramos que a atividade física aeróbia possui o potencial alterar a resposta de marcadores relacionados a resposta imune antitumoral. Em um primeiro estudo, observamos que a atividade física aeróbia atenuou o crescimento tumoral em três diferentes modelos de câncer em animais, assim como aumentou a sobrevida de animais com melanoma B16F10. Além disso, nós observamos que a atividade física aeróbia também altera a expressão gênica de marcadores de os linfócitos T infiltrantes de tumor (do inglês tumor infiltrating lymphocytes T - TILs-T) e de macrófagos associados ao tumor (do inglês tumor-associated macrophages - TAMs). Em segundo estudo, nosso objetivo foi explorar através de análises in silico respostas imunes tumorais que poderiam estar sendo moduladas pela atividade física aeróbia. Por meio da análise de Gene Set Enrichment Analysis (GSEA), observamos que a atividade física aeróbia prévia gerou uma assinatura imunológica tumoral semelhante à de pacientes com diferentes tipos de câncer e maior sobrevida. Essa assinatura imunológica revelou que pacientes com câncer de mama e melanoma que apresentam alta expressão gênica de BTG2, CD69, CFH, DUSP1 e PTGER4 em seus tumores, apresentam maior sobrevida em comparação aos pacientes com baixa expressão desses genes em seus tumores. A análise de GSEA também demonstrou que a atividade física aeróbia foi capaz de induzir uma assinatura imunológica semelhante à de animais com melanoma B16F10 sensíveis ao tratamento com o imunoterápico anti-CTLA-4 (do inglês anti- cytotoxic T-lymphocyte-associated antigen 4) e pacientes pós-tratamento com anti-CTLA-4. Essa assinatura imunológica também revelou que pacientes com melanoma que apresentam alta expressão gênica de PHC3, TET2, MACF1 e PARP8 em seus tumores, apresentam maior sobrevida em comparação aos pacientes com baixa expressão desses genes em seus tumores. Em conclusão, a atividade física aeróbia demonstra-se como uma potente ferramenta no combate a progressão da doença / It is already very well accepted that the aerobic physical activity reduces the incidence of many different types of cancer and mitigates the tumor progression. However, there is an investigation gap on the literature about the mechanisms underlying this response. Recently, a body of literature has arisen which show the importance of maintain antitumoral immune response to mitigated tumor progression, and strategy for enhance this response remains a major challenge. Presently, we demonstrate that the aerobic physical activity has the potential to modulate antitumor immune responses. Firstly, we have observed that the aerobic physical activity mitigated the tumor progression in three different animal models of cancer and increased survival in the animals which had B16F10 melanoma. Moreover, we have observed that the aerobic physical activity also seems modulate the tumor infiltrating T lymphocytes (TILs-T) and the tumor associated macrophages (TAMs) in as specific-tumor way. Based on the computer gene set enrichment analysis (GSEA), we have observed that the previous aerobic physical activity helped to develop an immunological signature of the tumor shared among patients with different cancer etiologies with higher survival over time. This signature introduced us to some genes that are associated with a higher survival over time when increased in tumors of patients with melanoma, breast cancer and lymphoma. The GSEA analysis also shows that the aerobic physical activity is able to introduce an immune signature similar to the one observed in animals with B16F10 melanoma sensitive to the immunotherapic anti- cytotoxic T-lymphocyte-associated antigen 4 (anti-CTLA-4) and patients post treatment with anti-CTLA-4. This immune signature also revealed genes with a strong association with high survival over time. This immune signature also revealed genes associated with a higher survival over time when their expression were increased in melanoma patients. In conclusion, aerobic physical activity is a powerful tool to counteract tumor progression due to its capacity to modulate antitumor immune responses

A atividade física aeróbia reduz a incidência de diversos tipos de câncer e atenua o crescimento tumoral. Entretanto, existe uma lacuna na literatura sobre os mecanismos envolvidos nessa resposta. Dados recentes demonstram a importância da manutenção da função da resposta imune antitumoral para a atenuação da progressão da doença e estratégias capazes de aumentar essa resposta permanecem como um grande desafio. Diante disso, delineamos um estudo experimental para investigar especificamente a possível contribuição da atividade física aeróbia para a resposta imune antitumoral. Na presente dissertação, nós demonstramos que a atividade física aeróbia possui o potencial alterar a resposta de marcadores relacionados a resposta imune antitumoral. Em um primeiro estudo, observamos que a atividade física aeróbia atenuou o crescimento tumoral em três diferentes modelos de câncer em animais, assim como aumentou a sobrevida de animais com melanoma B16F10. Além disso, nós observamos que a atividade física aeróbia também altera a expressão gênica de marcadores de os linfócitos T infiltrantes de tumor (do inglês tumor infiltrating lymphocytes T - TILs-T) e de macrófagos associados ao tumor (do inglês tumor-associated macrophages - TAMs). Em segundo estudo, nosso objetivo foi explorar através de análises in silico respostas imunes tumorais que poderiam estar sendo moduladas pela atividade física aeróbia. Por meio da análise de Gene Set Enrichment Analysis (GSEA), observamos que a atividade física aeróbia prévia gerou uma assinatura imunológica tumoral semelhante à de pacientes com diferentes tipos de câncer e maior sobrevida. Essa assinatura imunológica revelou que pacientes com câncer de mama e melanoma que apresentam alta expressão gênica de BTG2, CD69, CFH, DUSP1 e PTGER4 em seus tumores, apresentam maior sobrevida em comparação aos pacientes com baixa expressão desses genes em seus tumores. A análise de GSEA também demonstrou que a atividade física aeróbia foi capaz de induzir uma assinatura imunológica semelhante à de animais com melanoma B16F10 sensíveis ao tratamento com o imunoterápico anti-CTLA-4 (do inglês anti- cytotoxic T-lymphocyte-associated antigen 4) e pacientes pós-tratamento com anti-CTLA-4. Essa assinatura imunológica também revelou que pacientes com melanoma que apresentam alta expressão gênica de PHC3, TET2, MACF1 e PARP8 em seus tumores, apresentam maior sobrevida em comparação aos pacientes com baixa expressão desses genes em seus tumores. Em conclusão, a atividade física aeróbia demonstra-se como uma potente ferramenta no combate a progressão da doença / It is already very well accepted that the aerobic physical activity reduces the incidence of many different types of cancer and mitigates the tumor progression. However, there is an investigation gap on the literature about the mechanisms underlying this response. Recently, a body of literature has arisen which show the importance of maintain antitumoral immune response to mitigated tumor progression, and strategy for enhance this response remains a major challenge. Presently, we demonstrate that the aerobic physical activity has the potential to modulate antitumor immune responses. Firstly, we have observed that the aerobic physical activity mitigated the tumor progression in three different animal models of cancer and increased survival in the animals which had B16F10 melanoma. Moreover, we have observed that the aerobic physical activity also seems modulate the tumor infiltrating T lymphocytes (TILs-T) and the tumor associated macrophages (TAMs) in as specific-tumor way. Based on the computer gene set enrichment analysis (GSEA), we have observed that the previous aerobic physical activity helped to develop an immunological signature of the tumor shared among patients with different cancer etiologies with higher survival over time. This signature introduced us to some genes that are associated with a higher survival over time when increased in tumors of patients with melanoma, breast cancer and lymphoma. The GSEA analysis also shows that the aerobic physical activity is able to introduce an immune signature similar to the one observed in animals with B16F10 melanoma sensitive to the immunotherapic anti- cytotoxic T-lymphocyte-associated antigen 4 (anti-CTLA-4) and patients post treatment with anti-CTLA-4. This immune signature also revealed genes with a strong association with high survival over time. This immune signature also revealed genes associated with a higher survival over time when their expression were increased in melanoma patients. In conclusion, aerobic physical activity is a powerful tool to counteract tumor progression due to its capacity to modulate antitumor immune responses

O Diabetes Mellitus é uma das maiores causas de morte no mundo. O desenvolvimento do Diabetes Mellitus tipo 2 (DM2) está relacionado com uma série de fatores genéticos e ambientais, culminando com o desenvolvimento do DM2. Já a hiperglicemia, característica marcante da doença, está associada a uma série de complicações metabólicas e comorbidades. No entanto, nào se sabe a influência de um controle apropriado da doença, com menores níveis glicêmicos. No presente trabalho, foi utilizada a técnica de microarrays para comparar os perfis transcricionais (mRNA e microRNA) de células mononucleares de sangue periférico (PBMCs) em três grupos distintos: um grupo de pacientes DM2 descompensados (DM2-D, n=13); um grupo de pacientes DM2 compensados (DM2-C, n=14), e um grupo controle (n=10). Os dados foram analisados por meio de duas linguagens de programação: R e PERL. Após a extração dos dados utilizando-se o software Feature Extraction, versão 10.7 (Agilent Techonologies), foram realizadas correção do background, exclusão dos outliers, normalização dos dados pelo método quantile e, por fim, o ajuste de variações nãobiológicas. Os dados foram então submetidos a análise estatística rank products, sendo identificados 415 mRNAs diferencialmente expressos no grupo DM2-C relativamente aos controles, 285 no grupo DM2-D em comparação aos controles e 478 em pacientes DM2-D comparados aos DM2-C. Posteriormente, os genes diferencialmente expressos foram submetidos à analise de enriquecimento funcional (DAVID). Foram encontrados 22 e 56 termos biológicos enriquecidos (p-corrigido Benjamini-Hochberg < 0,05) para as comparações DM2-C versus controle e pacientes DM2-D versus DM2-C, respectivamente. Em ambas as comparações, um processo biológico foi considerado de interesse para o presente trabalho: resposta inflamatória. Na análise por GSEA e GSA, foram identificados 110 grupos gênicos diferencialmente expressos na comparação DM2-C versus controle. Já para a comparação DM2-D versus controles foram encontrados 297 grupos gênicos diferencialmente expressos, enquanto que na comparação DM2-D versus DM2-C, 161 grupos gênicos diferencialmente expressos. Dentre os grupos gênicos diferencialmente expressos, três merecem destaque: regulação do reparo do DNA (GO: 0006282), resposta ao superóxido (GO: 0000303) e resposta ao estresse do retículo endoplasmático (GO: 0034976). Ainda, 97 microRNAs foram diferencialmente expressos na comparação DM2-C versus controles, 54 na comparação DM2-D versus controles e 101 na comparação DM2-D versus DM2-C. Assim, diferentes grupos gênicos provavelmente foram modulados pela hiperglicemia, além de terem sido descobertos novos microRNAs relacionados a altos níveis de glicose. / Diabetes mellitus is a major cause of death worldwide. The development of type 2 Diabetes Mellitus (T2D) is associated with a number of genetic and environmental factors, culminating in the development of T2D. Hyperglycemia, a hallmark of the disease, is associated with a number of metabolic complications and comorbidities. However, the influence of a proper control of the disease, with lower glucose levels is unknown. In this study, we used the microarrays technique to compare the transcriptional profiles (mRNA and microRNA) of peripheral blood mononuclear cells (PBMCs) in three distinct groups: a group of patients with uncontrolled T2D patients (T2D-U, n = 13) a group of controlled T2D patients (T2D-C, n = 14) and control group (n = 10). Data were analyzed using two programming languages: R and PERL. After extracting the data using the Feature Extraction software, version 10.7 (Agilent Technologies), background correction, outliers exclusion, data normalization by quantile and adjustmesnt of non-biological variations were performed. The data were then statistically analyzed by the rank products test, which identified 415 differentially expressed mRNAs in T2D-C group compared to controls, 285 in group T2D-U in comparison with controls and 478 when T2D-U and T2D-C are compared. Thereafter, the differentially expressed genes were subjected to functional enrichment analysis (DAVID). 22 and 56 biologically enriched terms were found (Benjamini-Hochberg-corrected p value<0.05), when comparing T2D-C with controls and T2D-U with T2D-C, respectively. In both comparisons, inflammatory response was selected as a biological process of interest. The analysis by GSEA and GSA identified 110 differentially expressed gene sets in comparison T2D-C versus control. As for the comparison T2D-U versus control, 297 gene sets were found differentially expressed, whereas in comparison T2D-U versus T2D-C, 161 differentially expressed gene sets were found. Among the differentially expressed gene sets, three stand out: regulation of DNA repair (GO: 0006282), superoxide response (GO: 0000303) and response to endoplasmic reticulum stress (GO: 0034976). Still, 97 microRNAs were differentially expressed in the T2D-C versus controls comparison, 54 when comparing T2D-U versus controls and 101 in the comparison of T2D-U versus T2D-C. Thus, different gene sets were probably modulated by hyperglycemia, and new microRNAs related to high levels of glucose were discovered.

Sufficient vitamin D is important for overall health. However, cutaneous production of vitamin D is limited by season and little vitamin D naturally occurs in food. Therefore, vitamin D supplementation is necessary. Vitamin D is available in pharmacies as vitamin D2 and vitamin D3, and can also be obtained by irradiating mushrooms to produce vitamin D2. Types of vitamin D supplementation were tested to compare their ability to increase vitamin D status and their effect on broad gene expression in human white blood cells. 2000 IU of vitamin D2, vitamin D3 or vitamin D2 in irradiated mushroom powder were given to subjects daily for twelve weeks. A placebo mushroom powder group was included in the second half of the study. To determine the effect of different supplementation on vitamin D status, whole blood was obtained weekly and serum was assayed for 25(OH)D2 and 25(OH)D3. Change in total 25(OH)D was determined from baseline to twelve weeks; 25(OH)D levels in the placebo mushroom powder group did not change significantly at 1.8 ± 1.8 ng/ml (9.6 ± 9.6%), the mushroom D2 group increased by 10.9 ± 10.2 ng/ml (53.2 ± 49.8%), the supplemental D2 group increased by 11.8 ± 7.4 ng/ml (60.2 ± 37.8%) and the supplemental D3 group increased by 21.7 ± 8.9 ng/ml (114.2 ± 46.8%). As expected, the total active form of vitamin D (1,25-dihydroxyvitamin D) showed no change in all groups because of its tight regulation. To determine the potential influence of vitamin D supplementation on differential gene expression in the immune system, white blood cells were isolated from whole blood samples taken before and after supplementation. RNA was extracted, and microarray assays were performed. Gene Set Enrichment Analysis was completed to determine strongly influenced pathways. However, due to the numerous variables between halves of the study, gene expression data was treated as separate studies. Even so, pathways involving RNA activation and degradation were significant between mushroom powder and mushroom D2 supplementation in both halves of the study, indicating the influence of compounds in mushrooms on RNA metabolism pathways. Supplemental vitamin D2 affected gene expression, though only two pathways showed significant change. Supplemental vitamin D3 was found to influence pathways involved in replication, transcription, and translation in both halves of the study. In conclusion, mushrooms powder, mushroom vitamin D2, supplemental vitamin D2, and supplemental vitamin D3 all influence differential gene expression in human white blood cells.

RBMS3 belongs to the family of c-myc gene single-strand binding proteins (MSSPs) that play important roles in transcriptional regulation. Here, we show that RBMS3 functions as a tumor promoter in triple-negative breast cancer (TNBC), a highly aggressive BC subtype. Analysis of RBMS3 expression shows that RBMS3 is upregulated at both mRNA and protein levels in TNBC cells. Functionally, overexpression of RBMS3 increases cell migration, invasion and cancer stem cell (CSC) behaviors. Moreover, RBMS3 induces expression of epithelial-mesenchymal transition (EMT) and CSC markers. Conversely, loss of RBMS3 in TNBC BT549 cells inhibits cell proliferation, migration and mesenchymal phenotype. Correlation analysis shows RBMS3 is associated with TGF-β signaling. Mechanistically, RBMS3 interacts with Smad2, Smad3 and Smad4 mRNA and regulates the stability of these transcripts. Importantly, RBMS3 prevents TGF-β-induced cytostasis and apoptosis in premalignant cancer cells. Moreover, RBMS3 inversely correlates with expression of ESRPs, epithelial-specific splicing regulatory proteins that regulate morphogenesis-associated alternative splicing events. ESRPs appear to suppress EMT through distinct mechanisms: ESRP1 restricted cell migration, whereas ESRP2 prevented cell growth. RBMS3 significantly facilitates the EMT process when ESRPs are lost. Collectively, the studies within this dissertation identify RBMS3 as a positive regulator of EMT and breast cancer progression by regulating the TGF-β signaling pathway.

<p>The use of traditional univariate analyses for comparing groups in high-dimensional genomic studies, such as the ordinary t-test that is typically used to compare two independent groups, might be suboptimal because of methodological challenges including multiple testing problem and failure to incorporate correlation among genes. Hence, multivariate methods are preferred for the joint analysis of a group or set of variables. These methods aim to test for differences in average values of a set of variables across groups. The variables that make the set could be determined statistically (using exploratory methods such as cluster analysis) or biologically (based on membership to known pathways). In this thesis, the traditional One-Way Multivariate Analysis of Variance (MANOVA) method and a robustifed version of MANOVA (Robustifed MANOVA) are compared with respect to Type I error rates and power through a simulation study. We generated data from multivariate normal as well as multivariate gamma distributions with different parameter settings. The methods are illustrated using a real gene expression data. In addition, we investigated a popular method known as Gene Set Enrichment Analysis (GSEA), where sets of genes (variables) that belong to known biological pathways are considered jointly and assessed whether or not they are "enriched" with respect to their association with a disease or phenotype of interest. We applied this method to a real genotype data.</p> / Master of Science (MSc)

<p dir="ltr">The genetic perturbations caused by spaceflight on biological systems tend to have a system-wide effect which is often difficult to deconvolute into individual signals with specific points of origin. Single cell multi-omic data can provide a profile of the perturbational effects but does not necessarily indicate the initial point of interference within a network. The objective of this project is to take advantage of large scale and genome-wide perturbational or Perturb-Seq datasets by using them to pre-train a generalist machine learning model that is capable of predicting the effects of unseen perturbations in new data. Perturb-Seq datasets are large libraries of single cell RNA sequencing data collected from CRISPR knock out screens in cell culture. The advent of generative machine learning algorithms, particularly transformers, make it an ideal time to re-assess large scale data libraries in order to grasp cell and even organism-wide genomic expression motifs. By tailoring an algorithm to learn the downstream effects of the genetic perturbations, we present a pre-trained generalist model capable of predicting the effects of multiple perturbations in combination, locating points of origin for perturbation in new datasets, predicting the effects of known perturbations in new datasets, and annotation of large-scale network motifs. We demonstrate the utility of this model by identifying key perturbational signatures in RNA sequencing data from spaceflown biological samples from the NASA Open Science Data Repository.</p>