In every living cell, millions of different types of molecules constantly interact and react chemically in a complex system that can adapt to fluctuating environments and extreme conditions, living to survive and reproduce itself. The information required to produce these components is stored in the genome, which is copied in each cell division and transferred and mixed with another genome from parent to child. The regulatory mechanisms that control biological systems, for instance the regulation of expression levels for each gene, has evolved so that global robustness and ability to survive under harsh conditions is a strength, at the same time as biological tasks on a detailed molecular level must be carried out with good precision and without failures. This has resulted in systems that can be described as a hierarchy of levels of complexity: from the lowest level, where molecular mechanisms control other components at the same level, to pathways of coordinated interactions between components, formed to carry out particular biological tasks, and up to large-scale systems consisting of all components, connected in a network with a topology that makes the system robust and flexible. This thesis reports on work that model and analyze complex biological systems, and the signals and noise that regulate them, at all different levels of complexity. Also, it shows how signals are transduced vertically from one level to another, as when a single mutation can cause errors in low level mechanisms, disrupting pathways and create systemwide imbalances, such as in type 2 diabetes. The advancement of our knowledge of biological systems requires both that we go deeper and towards more detail, of single molecules in single cells, as well as taking a step back to understand the organisation and dynamics in the large networks of all components, and unite the different levels of complexity.

The Pezizomycotina, commonly known as the filamentous fungi, are a diverse group of organisms that have a major impact on human life. The filamentous fungi diverged from a common ancestor approximately 200 – 700 million years ago. Because of the diversity and the wealth of biological and genomic tools for the filamentous fungi it is possible to track the evolutionary history of genes and gene networks in these organisms. In this dissertation I focus on the evolution of two genes (lolC and lolD) in the LOL secondary metabolite gene cluster in Epichloë and Neotyphodium genera, the evolution of the MAP kinase-signaling cascade in the filamentous fungi, the regulation of the gene networks involved in asexual development in Neurospora crassa, and the identification of two genes in the N. crassa asexual development gene network, acon-2 and acon-3. I find that lolC and lolD originated as an ancient duplication in the ancestor of the filamentous fungi, which were later recruited in the LOL gene cluster in the fungal endophyte lineage. In the MAP kinase-signaling cascade, I find that the MAPK component is the most central gene in the gene network. I also find that the MAPK signaling cascade originated as three copies in the ancestor to eukaryotes, an arrangement that is maintained in filamentous fungi. My observations of gene expression profiling during N. crassa asexual development show tissue specific expression of genes. Both the vegetative mycelium and the aerial hyphae contribute to the formation of macroconidiophores. Also, with the help of genomic tools recently developed by researchers in the filamentous fungal community, I identified NCU00478 and NCU07617 as the genes with mutations responsible for two aconidial strains of N. crassa, acon-2 and acon-3 respectively.

To understand how the components of a complex system like the biological cell interact and regulate each other, we need to collect data for how the components respond to system perturbations. Such data can then be used to solve the inverse problem of inferring a network that describes how the pieces influence each other. The work in this thesis deals with modelling the cell regulatory system, often represented as a network, with tools and concepts derived from systems biology. The first investigation focuses on network sparsity and algorithmic biases introduced by penalised network inference procedures. Many contemporary network inference methods rely on a sparsity parameter such as the L1 penalty term used in the LASSO. However, a poor choice of the sparsity parameter can give highly incorrect network estimates. In order to avoid such poor choices, we devised a method to optimise the sparsity parameter, which maximises the accuracy of the inferred network. We showed that it is effective on in silico data sets with a reasonable level of informativeness and demonstrated that accurate prediction of network sparsity is key to elucidate the correct network parameters. The second investigation focuses on how knowledge from association networks can be transferred to regulatory network inference procedures. It is common that the quality of expression data is inadequate for reliable gene regulatory network inference. Therefore, we constructed an algorithm to incorporate prior knowledge and demonstrated that it increases the accuracy of network inference when the quality of the data is low. The third investigation aimed to understand the influence of system and data properties on network inference accuracy. L1 regularisation methods commonly produce poor network estimates when the data used for inference is ill-conditioned, even when the signal to noise ratio is so high that all links in the network can be proven to exist for the given significance. In this study we elucidated some general principles for under what conditions we expect strongly degraded accuracy. Moreover, it allowed us to estimate expected accuracy from conditions of simulated data, which was used to predict the performance of inference algorithms on biological data. Finally, we built a software package GeneSPIDER for solving problems encountered during previous investigations. The software package supports highly controllable network and data generation as well as data analysis and exploration in the context of network inference. / <p>At the time of the doctoral defense, the following paper was unpublished and had a status as follows: Paper 4: Manuscript.</p><p> </p>

Arsenic is a universal environmental toxicant associated mostly with skin related diseases in people exposed to low doses over a long term. Low dose arsenic trioxide (ATO) with long exposure will lead to chronic exposure. Experiments were performed to provide new knowledge on the incompletely understood mechanisms of action of chronic low dose inorganic arsenic in keratinocytes. Cytotoxicity patterns of ATO on long-term cultures of HaCaT cells on collagen IV was studied over a time course of 14 days. DNA damage was also assessed. The percentages of viable cells after exposure were measured on Day 2, Day 5, Day 8, and Day 14. Statistical and visual analytics approaches were used for data analysis. In the result, a biphasic toxicity response was observed at a 5 μg/ml dose with cell viability peaking on Day 8 in both chronic and acute exposures. Furthermore, a low dose of 1 μg/ml ATO enhanced HaCaT keratinocyte proliferation but also caused DNA damage. Global gene expression study using microarray technique demonstrated differential expressions of genes in HaCaT cell exposed to 0.5 μg/ml dose of ATO up to 22 passages. Four of the up-regulated and 1 down-regulated genes were selected and confirmed with qRT-PCR technique. These include; Aldo-Keto Reductase family 1, member C3 (AKR1C3), Insulin Growth Factor-Like family member 1 (IGFL1), Interleukin 1 Receptor, type 2 (IL1R2) and Tumour Necrosis Factor [ligand] Super-Family, member 18 (TNFSF18), and down-regulated Regulator of G-protein Signalling 2 (RGS2). The decline in growth inhibiting gene (RGS2) and increase in AKR1C3 may be the contributory path to chronic inflammation leading to metaplasia. This pathway is proposed to be a mechanism leading to carcinogenesis in skin keratinocytes. The observed over expression of IGFL1 may be a means of triggering carcinogenesis in HaCaT keratinocytes. In conclusion, it was established that at very low doses, arsenic is genotoxic and induces aberrations in gene expression though it may appear to enhance cell proliferation. The expression of two genes encoding membrane proteins IL1R2 and TNFSF18 may serve as possible biomarkers of skin keratinocytes intoxication due to arsenic exposure. This research provides insights into previously unknown gene markers that may explain the mechanisms of arsenic-induced dermal disorders including skin cancer / Environmental Sciences / D. Phil. (Environmental science)

Orientador: Prof. Dr. David Corrêa Martins Júnior / Dissertação (mestrado) - Universidade Federal do ABC, Programa de Pós-Graduação em Ciência da Computação, 2017.

The rate at which articles gets published grows exponentially and the possibility to access texts in machine-readable formats is also increasing. The need of an automated system to gather relevant information from text, text mining, is thus growing. The goal of this thesis is to find a biologically relevant gene network for atherosclerosis, themain cause of cardiovascular disease, by inspecting gene cooccurrences in abstracts from PubMed. In addition to this gene nets for yeast was generated to evaluate the validity of using text mining as a method. The nets found were validated in many ways, they were for example found to have the well known power law link distribution. They were also compared to other gene nets generated by other, often microbiological, methods from different sources. In addition to classic measurements of similarity like overlap, precision, recall and f-score a new way to measure similarity between nets are proposed and used. The method uses an urn approximation and measures the distance from comparing two unrelated nets in standard deviations. The validity of this approximation is supported both analytically and with simulations for both Erd¨os-R´enyi nets and nets having a power law link distribution. The new method explains that very poor overlap, precision, recall and f-score can still be very far from random and also how much overlap one could expect at random. The cutoff was also investigated. Results are typically in the order of only 1% overlap but with the remarkable distance of 100 standard deviations from what one could have expected at random. Of particular interest is that one can only expect an overlap of 2 edges with a variance of 2 when comparing two trees with the same set of nodes. The use of a cutoff at one for cooccurrence graphs is discussed and motivated by for example the observation that this eliminates about 60-70% of the false positives but only 20-30% of the overlapping edges. This thesis shows that text mining of PubMed can be used to generate a biologically relevant gene subnet of the human gene net. A reasonable extension of this work is to combine the nets with gene expression data to find a more reliable gene net.

No description available.

INTRODUÇÃO: O polimorfismo dentro do gene TCF7L2, rs7903146, é, até o momento, o marcador genético mais significantemente associado ao risco de diabetes mellitus tipo 2, sendo também associado à doença arterial coronariana. Contudo, pouco ainda se conhece sobre o papel funcional desse polimorfismo na patologia dessas doenças. O objetivo desse projeto foi investigar esse papel funcional, no fenótipo de células vasculares de músculo liso de 92 indivíduos, usando abordagens de comparação de níveis de expressão gênica e de comparação de correlações de expressão gênica, de modo que tais comparações fossem representadas visualmente como redes de interação gênica. MÉTODOS: Inicialmente, foram comparados os níveis de expressão de 41 genes (genes que possuem ou estão perto de variantes genéticas associadas ao diabetes mellitus tipo 2 e outros genes relacionados às vias de sinalização de diabetes mellitus tipo 2 ou às vias de proliferação celular) entre indivíduos com o alelo associado ao risco de diabetes mellitus tipo 2 (CT e TT) e indivíduos sem o alelo de risco (CC) do rs7903146. Com a finalidade de se observar se os genes estavam se relacionando de modo diferente entre os grupos genotípicos, foram comparados os padrões de correlação de expressão dos 41 genes. RESULTADOS: Quanto às comparações de níveis de expressão entre os grupos, cinco formas de splicing do gene TCF7L2 e os genes CDKAL1, IGF2BP2, JAZF1, CDKN2B, CAMK1D, JUN, CDK4, ATP2A2, e FKBP1A apresentaram níveis de expressão significativamente diferentes. Quanto às comparações de correlação de expressão entre os grupos, os genes RXR?, CALM1, CALR e IGF2BP2 foram os que mostraram os mais diferentes padrões de correlação com os outros genes. CONCLUSÃO: Deste modo, o alelo de risco analisado é apontado como tendo influência em cis na regulação da expressão de determinadas formas de splicing do gene TCF7L2 em células vasculares de músculo liso; além de parecer influenciar nas expressões e nas interações de genes relacionados à homeostase glicolítica e/ou proliferação celular. Sendo assim, através de nossas análises identificaram-se possíveis candidatos-alvos no tratamento de redução do risco em indivíduos com alto risco de desenvolvimento de diabetes mellitus tipo 2 e de doença arterial coronariana, especialmente os indivíduos que possuem os genótipos de risco analisados do gene TCF7L2 / INTRODUCTION: The SNP within the TCF7L2 gene, rs7903146, is, to date, the most significant genetic marker associated with type 2 diabetes mellitus risk, well as being associated with coronary artery disease. Nonetheless, its functional role in these diseases pathology is poorly understood. The aim of the present study was to investigate this role, in vascular smooth muscle cells from 92 patients undergoing aortocoronary bypass surgery, using expression levels and expression correlation comparison approaches, which were visually represented as gene interaction networks. METHODS: Initially, the expression levels of 41 genes (seven TCF7L2 splice forms and other 40 relevant genes) were compared between rs7903146 wild-type (CC) and type 2 diabetes mellitus risk (CT + TT) genotype groups. Next, the expression correlation patterns of the 41 genes were compared between genotypic groups in order to observe if the relationships between genes were different. RESULTS: Five TCF7L2 splice forms and CDKAL1, IGF2BP2, JAZF1, CDKN2B, CAMK1D, JUN, CDK4, ATP2A2 and FKBP1A genes showed significant expression differences between groups. RXR?, CALM1, CALR and IGF2BP2 genes were pinpointed as showing the most different expression correlation pattern with other genes. CONCLUSION: Therefore, type 2 diabetes mellitus risk alleles appear to be influencing TCF7L2 splice form\'s expression in vascular smooth muscle cells; besides it can be influencing expression and interactions of genes related to glucose homeostasis and/or cellular proliferation. Thereby, through our analysis were identified possible treatment target candidates for risk reduction in individuals with high-risk of developing type 2 diabetes mellitus and coronary artery disease, especially individuals harboring TCF7L2 risk genotypes

INTRODUÇÃO: O polimorfismo dentro do gene TCF7L2, rs7903146, é, até o momento, o marcador genético mais significantemente associado ao risco de diabetes mellitus tipo 2, sendo também associado à doença arterial coronariana. Contudo, pouco ainda se conhece sobre o papel funcional desse polimorfismo na patologia dessas doenças. O objetivo desse projeto foi investigar esse papel funcional, no fenótipo de células vasculares de músculo liso de 92 indivíduos, usando abordagens de comparação de níveis de expressão gênica e de comparação de correlações de expressão gênica, de modo que tais comparações fossem representadas visualmente como redes de interação gênica. MÉTODOS: Inicialmente, foram comparados os níveis de expressão de 41 genes (genes que possuem ou estão perto de variantes genéticas associadas ao diabetes mellitus tipo 2 e outros genes relacionados às vias de sinalização de diabetes mellitus tipo 2 ou às vias de proliferação celular) entre indivíduos com o alelo associado ao risco de diabetes mellitus tipo 2 (CT e TT) e indivíduos sem o alelo de risco (CC) do rs7903146. Com a finalidade de se observar se os genes estavam se relacionando de modo diferente entre os grupos genotípicos, foram comparados os padrões de correlação de expressão dos 41 genes. RESULTADOS: Quanto às comparações de níveis de expressão entre os grupos, cinco formas de splicing do gene TCF7L2 e os genes CDKAL1, IGF2BP2, JAZF1, CDKN2B, CAMK1D, JUN, CDK4, ATP2A2, e FKBP1A apresentaram níveis de expressão significativamente diferentes. Quanto às comparações de correlação de expressão entre os grupos, os genes RXR?, CALM1, CALR e IGF2BP2 foram os que mostraram os mais diferentes padrões de correlação com os outros genes. CONCLUSÃO: Deste modo, o alelo de risco analisado é apontado como tendo influência em cis na regulação da expressão de determinadas formas de splicing do gene TCF7L2 em células vasculares de músculo liso; além de parecer influenciar nas expressões e nas interações de genes relacionados à homeostase glicolítica e/ou proliferação celular. Sendo assim, através de nossas análises identificaram-se possíveis candidatos-alvos no tratamento de redução do risco em indivíduos com alto risco de desenvolvimento de diabetes mellitus tipo 2 e de doença arterial coronariana, especialmente os indivíduos que possuem os genótipos de risco analisados do gene TCF7L2 / INTRODUCTION: The SNP within the TCF7L2 gene, rs7903146, is, to date, the most significant genetic marker associated with type 2 diabetes mellitus risk, well as being associated with coronary artery disease. Nonetheless, its functional role in these diseases pathology is poorly understood. The aim of the present study was to investigate this role, in vascular smooth muscle cells from 92 patients undergoing aortocoronary bypass surgery, using expression levels and expression correlation comparison approaches, which were visually represented as gene interaction networks. METHODS: Initially, the expression levels of 41 genes (seven TCF7L2 splice forms and other 40 relevant genes) were compared between rs7903146 wild-type (CC) and type 2 diabetes mellitus risk (CT + TT) genotype groups. Next, the expression correlation patterns of the 41 genes were compared between genotypic groups in order to observe if the relationships between genes were different. RESULTS: Five TCF7L2 splice forms and CDKAL1, IGF2BP2, JAZF1, CDKN2B, CAMK1D, JUN, CDK4, ATP2A2 and FKBP1A genes showed significant expression differences between groups. RXR?, CALM1, CALR and IGF2BP2 genes were pinpointed as showing the most different expression correlation pattern with other genes. CONCLUSION: Therefore, type 2 diabetes mellitus risk alleles appear to be influencing TCF7L2 splice form\'s expression in vascular smooth muscle cells; besides it can be influencing expression and interactions of genes related to glucose homeostasis and/or cellular proliferation. Thereby, through our analysis were identified possible treatment target candidates for risk reduction in individuals with high-risk of developing type 2 diabetes mellitus and coronary artery disease, especially individuals harboring TCF7L2 risk genotypes

The overall objective of this dissertation was to examine exposures to the estrogenic endocrine disrupting chemicals (EEDCs), phthalates, bisphenol-A (BPA), and the metalloestrogens cadmium (Cd), arsenic (As), and manganese (Mn) in an older geriatric aged-population and examine associations with brain health. Given the evidence that EEDCs affect brain health and play a role in the development of cognitive dysfunction and neurodegenerative disease, and the constant environmental exposure through foods and everyday products has led this to becoming a great public health concern. Using a bioinformatic approach to find nuclear respiratory factor 1 (NRF1) gene targets involved in mitochondrial dysfunction, that are both estrogen and EEDC-sensitive, we found several genes involved in the gene pathways of Alzheimer’s disease (AD): APBB2, EIF2S1, ENO1, MAPT, and PAXIP1. Using the Center for Disease Control and Prevention (CDC), National Health and Nutrition Examination Survey (NHANES) 2011-2014 datasets to assess EEDC bioburden and associations with surrogate indicators of brain health, which include cognitive scores, memory questions, and taste and smell data, we found phthalate bioburden to be significantly higher in those with adverse brain health vii and significantly higher in females. In our logistic regression model when controlling for all known and suspected covariates in AD, in females, the phthalates in females ECP, MBP, MOH, MZP, and MIB in males and the phthalates COP, ECP, MBP, MC1, MEP, MHH, MOH, and MIB were significantly associated with poor cognitive test scores, poor memory, and taste and smell dysfunction. Among the metalloestrogens, Cd bioburden was higher in those with poor cognitive performance, poor memory, and taste and smell dysfunction, with the trend more significant in males. Among oral contraceptive (OC) and HRT (hormone replacement therapy) use, in our logistic regression model when controlling for all known and suspected covariates in AD, past OC and HRT use was associated with better cognitive test scores. The study provides further evidence of the complex role EEDCs play in overall brain health through other biological mechanisms and fills a gap in knowledge that demonstrates EEDCs effects on brain health in a geriatric age population.