Microarray technology

The normal functions of the cells are based on a strict and regulated expression of various genes. If this precise hierarchy of gene actions becomes unregulated or disturbed due to different genetic or environmental effects, the result will be abnormal cellular function that eventually could lead pathological alterations, including carcinogenic transformation or apoptosis. To understand the complex mechanisms and networks involved in biological processes and diseases, it is not enough to analyze isolated pathways, single gene functions or a single genetic event. A living organism has to be studied as a complex system and all genes involved in different biological processes need to be analyzed simultaneously: a systems biology approach should be applied. In the beginning of the 1990’s years, a new, high throughput technology - called microarray technology – was developed to measure the expression levels of large numbers of genes simultaneously or to genotype multiple regions of a genome. Microarrays have dramatically accelerated many types of investigation since a microarray experiment can accomplish many genetic tests in parallel. This review summarizes some of aspects of the microarray technology, including sample preparations, application possibilities and data analysis

Epigenetic biomarkers as predictors of clinical outcomes in colorectal cancer

Colorectal Cancer is the third most common cancer and the second leading cause of death by cancer worldwide with about 1.3 million new cancer cases and 693,933 deaths reported in 2012. Here, we intend to determine an epigenetic roadmap of Colorectal Cancer to predict tumor progression and patient outcome. We analyzed whole-genome DNA methylation (Illumina Infinium HumanMethylation 450K array) and gene expression (Illumina HiSeq) in multiple stages of CRC (21 normal, 54 stage I, 131 stage II, 111 stage III, and 51 stage IV). The data is available in TCGA database, and was downloaded, processed and analyzed through R programming. Results show that, in stages I, II, III, and IV, 307, 400, 305 and 233 genes are differentially expressed (fold-change absolute value > 1.5, p-value adjusted 0.2, p-value adjusted<0.05), respectively. In addition, all these CpG sites are correlated with the respective gene. When the KEGG and Gene Ontology analysis was performed, we found that the enriched functions are related to nervous system, one of the processes deregulated in cancer progression. Moreover, we also identified 66, 85, 41, and 40 specific genes for stages I, II, III, and IV, respectively. Regarding the diagnosis, were found 238 genes and 835 CpG sites as good diagnosis tool for stage I (AUC>0.8). Furthermore, 6, 1, and 5 genes and 87, 7, and 3 CpG sites were classified as good biomarkers for overall survival for stages I-IV, respectively. In addition, 3, 3, and 2 genes and 30, 12, 9 CpG sites were identified as good biomarkers for recurrence free survival for stages I-IV, respectively. These results suggest that different methylation events are associated to specific stages of CRC which can predict patient outcome and might improve colorectal cancer diagnosis and prognosis.O cancro colorretal é um evento biológico que compreende múltiplos passos, decorrendo de diversas alterações genéticas e epigenéticas. Apesar das melhorias no rastreio, diagnóstico e prognóstico de cancro, incluindo de cancro colorretal, este continua a ser o terceiro tipo de cancro mais comum em homens e segundo em mulheres, com mais de 1,3 milhões de novos casos diagnosticados, e 693.933 mortes reportados em todo o mundo no ano de 2012. Em parte, a incidência e mortalidade continuam elevadas devido à baixa sensibilidade e especificidade na deteção de cancro colorretal nos estádios iniciais da doença. Atualmente, entre os diversos meios de diagnóstico, a técnica mais eficiente é a colonoscopia, contudo apresenta baixa especificidade e sensibilidade. Estudos mais recentes têm apontado outros biomarcadores como forma de diagnóstico e prognóstico para o cancro colorretal, incluindo a septina 9. Este último é um biomarcador epigenético atualmente comercializado. Este projeto teve como objetivos realizar uma análise global do genoma em termos de metilação do ADN e expressão genética através de um código em R, identificar mutações epigenéticas que ocorram ao longo da progressão do cancro colorretal, e, por último, relacionar estas alterações com o efeito causado nos doentes. Métodos: Neste projeto, foi efetuada uma análise global do genoma de um cohort de cancro colorretal, em termos de metilação do ADN (Illumina Infinium HumanMethylation 450K array) e expressão genética (Illumina HiSeq). Neste projeto, foram analisadas 21 amostras de tecido normal adjacente ao tumor e 347 amostras tumorais divididas de acordo com a classificação TNM (54 estadio I, 131 estadio II, 111 estadio III e 51 estadio IV). Estes dados estão publicamente disponíveis, sendo que foram descarregados da base de dados do The Cancer Genome Atlas (TCGA) e analisados através de programação em R. Resultados: Os resultados sugerem que nos estádios I, II, III e IV, estão diferencialmente expressos 307, 400, 305 e 233 genes (valor absoluto de fold-change > 1,5 e p-value ajustado (FDR) 0,2 e p-value ajustado (FDR) < 0.05), respetivamente. Em adição, cada um destes locais de metilação encontra-se correlacionado com os respetivos genes encontrados diferencialmente expressos no mesmo estadio (p-value < 0.05). De seguida, efetuou-se uma análise nas bases de dados KEGG e Gene Ontology (GO). A utilização destas ferramentas revelou que as funções mais enriquecidas estão relacionadas com o sistema nervoso. Estudos anteriores já tinham descrito alterações em genes envolvidos no desenvolvimento e regulação do sistema nervoso como desreguladas em diversos tipos de cancro. Em adição, foi ainda realizada uma análise com o objetivo de encontrar quais dos genes encontrados diferencialmente expressos e que continham locais de metilação diferencialmente metilados ainda não tinham sido reportados em associação com cancro colorretal e cancro em geral. Esta análise sugere que 87 genes nunca foram associados nem com cancro colorretal nem com cancro no geral. Em oposição, 511 já forma reportados em algum tipo de cancro. Destes últimos, 278 já foram também reportados em cancro colorretal enquanto 233 nunca foram descritos neste tipo de cancro. Como forma de validação, realizou-se, ainda, uma técnica multivariada de representação gráfica, a qual demonstrou que tanto os genes como os locais de metilação selecionados conseguem distinguir amostras tumorais de amostras normais. Esta técnica permitiu-nos ainda diferenciar amostras tumorais em dois grupos principais distintos. Ainda neste estudo, foram identificados 66, 85, 41 e 40 genes que estão somente diferencialmente expressos nos estádios I, II, III e IV. Curiosamente, apenas 85 genes são comuns aos 4 estadios de desenvolvimento de cancro colorretal O potencial dos genes e locais de metilação, encontrados como diferencialmente expressos e metilados, respetivamente, para distinguir tecido tumoral do tecido normal também foi avaliado através da análise de curvas de receiver operating characteristic (ROC). Como resultado, obteve-se que 238 genes e 835 locais de metilação são bons marcadores de tecido tumoral em estadio I, quando comparado com tecido normal adjacente (AUC > 0,8, sendo que apenas foram selecionados os pontos ótimos com especificidade e sensibilidade > 60%). ASTN1, por exemplo, foi um dos genes classificados como um excelente marcador de diagnóstico (AUC =0,989). Este gene contém ainda o local de metilação cg08104310, o qual foi considerado um excelente marcador de diagnóstico (AUC=1,000). De seguida, a capacidade de prever o outcome do paciente em termos de sobrevida em geral e sobrevida livre de progressão, através dos valores de metilação e expressão dos genes e locais de metilação específicos para cada um dos estádios, foi avaliada. Em relação à sobrevivência em geral, para os estádios II, III e IV, foram identificados 6, 1 e 5 genes e 87, 7 e 3 locais de metilação, respetivamente, como possíveis biomarcadores de prognóstico (p-value < 0.05). Especificamente, genes como o ZNF536 (p-value=0,018; HR=3,133), SOX1 (p-value=0,041; HR=0.459) e BFSP2 (p-value=0,027; HR=2.828), por exemplo, foram identificados como bons preditores de sobrevivência em geral dos estádios II, III e IV, respetivamente. Relativamente aos locais de metilação, as cg02430935 localizada no gene HMX (p-value=0,013; HR=3,139), cg26489108 localizada no gene DMRT3 (p-value=0,027; HR=0,407) e a cg01847754 localizada no gene CXorf1 (p-value=0,019; HR=3,155), por exemplo, foram identificadas como bons marcadores para a sobrevivência em geral dos estádios II, III e IV, respetivamente. Quanto à sobrevivência livre de recorrência, para os estádios II, III e IV, foram identificados 3, 3 e 2 genes e 30, 12 e 9 locais de metilação, respetivamente, capazes de prever se o doente para recorrer ou não. Mais concretamente, genes como o CNTD2 (p-value=0,00033; HR=0,196), SOX1 (p-value=0.01; HR=0,359) e HTR2C (p-value=0,0064; HR=0,285) foram identificados como bons preditores de prognóstico para a sobrevivência livre de progressão nos estádios II, III e IV, respetivamente. Relativamente aos locais de metilação, as cg06162589 localizada no gene SLC5A8 (p-value=0.0066; HR=0,2924), cg03700449 localizada no gene ASCL1 (p-value=0.0055; HR=0,3114) e cg14772660 localizada no gene SLC5A7 (p-value=0.0047; HR=4,3174) são exemplos de bons preditores de sobrevivência livre de progressão para os estádios II, III e IV, respetivamente. Conclusão: Este estudo sugere que as alterações epigenéticas são dinâmicas ao longo da progressão de cancro colorretal, demonstrando que há alterações que são características de estádios específicos, enquanto outras se mantêm alteradas desde o primeiro estadio. Notavelmente, algumas das alterações conseguem distinguir doentes com um prognóstico mais severo de doentes com um prognóstico mais indolente. Assim sendo, este estudo mostrou que existem possíveis biomarcadores para cancro colorretal que devem ser melhor estudados no futuro. Este estudo pode ainda demarcar o início da melhoria das técnicas de diagnóstico e prognóstico

Deciphering causal genetic determinants of red blood cell traits

Les études d’association pan-génomiques ont révélé plusieurs variants génétiques associés à des traits complexes. Les mesures érythrocytaires ont souvent fait l’objet de ce genre d’études, étant mesurées de façon routinière et précise. Comprendre comment les variations génétiques influencent ces phénotypes est primordial étant donné leur importance comme marqueurs cliniques et leur influence sur la sévérité de plusieurs maladies. En particulier, des niveaux élevés d’hémoglobine fœtal chez les patients atteints d’anémie falciforme est associé à une réduction des complications et une augmentation de l’espérance de vie. Néanmoins, la majorité des variants génétiques identifiés par ces études tombent à l’intérieur de régions génétiques non-codantes, augmentant la difficulté d’identifier des gènes causaux. L’objectif premier de ce projet est l’identification et la caractérisation de gènes influençant les traits complexes, et tout particulièrement les traits sanguins. Pour y arriver, j’ai tout d’abord développé une méthode permettant d’identifier et de tester l’effet de gènes knockouts sur les traits anthropométriques. Malgré un échantillon de grande taille, cette approche n’a révélé aucune association. Ensuite, j’ai caractérisé le méthylome et le transcriptome d’érythroblastes différentiés à partir de cellules souches hématopoïétiques et identifié plusieurs gènes potentiellement impliqués dans les programmes érythroïdes fœtaux et adultes. Par ailleurs, j’ai identifié plusieurs micro-ARNs montrant des motifs d’expression spécifiques entre les stages fœtaux et adultes et qui sont enrichis pour des cibles exprimées de façon opposée. Finalement, j’ai identifié plusieurs variants génétiques associés à l’expression de gènes dans les érythroblastes (eQTL). Cette étude a permis d’identifier des variants associés à l’expression du gène ATP2B4, qui encode le principal transporteur de calcium des érythrocytes. Ces variants, qui sont également associés à des traits sanguins et à la susceptibilité à la malaria, tombent dans un élément d’ADN spécifique aux cellules érythroïdes. La délétion de cet élément par le système CRISPR/Cas9 induit une forte diminution de l’expression du gène et une augmentation des niveaux de calcium intracellulaires. En conclusion, des échantillons de génotypages exhaustifs seront nécessaires pour étudier l’effet de gènes knockouts sur les traits complexes. Les érythroblastes montrent de grandes différences au niveau de leur méthylome et transcriptome entre les différents stages développementaux. Ces différences influencent potentiellement la régulation de l’hémoglobine fœtale et impliquent de nombreux micro-ARNs et régions régulatrices non-codantes. Finalement, l’exemple d’ATP2B4 montre qu’intégrer des études épigénomiques, transcriptomiques et des expériences d’édition de génome est une approche puissante pour caractériser des variants génétiques non-codants. Par ailleurs, ces résultats impliquent ATP2B4 dans l’hydratation des érythroblastes, qui est associé à la susceptibilité à la malaria et la sévérité de l’anémie falciforme. Cibler ATP2B4 de façon thérapeutique pourrait avoir un impact majeur sur ces maladies qui affectent des millions d’individus à travers le monde.Genome-wide association studies (GWAS) have revealed several genetic variants associated with complex phenotypes. This is the case for red blood cell (RBC) traits, which are particularly amenable to GWAS as they are routinely and accurately measured. Understanding RBC trait variation is important given their significance as clinical markers and modifiers of disease severity. Notably, increased fetal hemoglobin (HbF) production in sickle cell disease (SCD) patients is associated with a higher life expectancy and decreased morbidity. Nonetheless, most variants identified through GWAS fall in non-coding regions of the human genome, increasing the difficulty of identifying causal links. The main goal of this project was to identify and characterize genes influencing complex traits, and in particular RBC phenotypes. First, I developed an approach to identify and test potential gene knockouts affecting anthropometric traits in a large sample from the general population, which did not yield significant associations. Then, I characterized the DNA methylome and transcriptome of erythroblasts differentiated ex vivo from hematopoietic progenitor stem cells (HPSC), and identified several genes potentially implicated in fetal and adult-stage erythroid programs. I also identified microRNAs (miRNA) that show specific developmental expression patterns and that are enriched in inversely expressed targets. Finally, I mapped expression quantitative trait loci (eQTL) in erythroblasts, and identify erythroid-specific eQTLs for ATP2B4, the main calcium ATPase of RBCs. These genetic variants are associated with RBC traits and malaria susceptibly, and overlap an erythroid-specific enhancer of ATP2B4. Deletion of this regulatory element using CRISPR/Cas9 experiments in human erythroid cells minimized ATP2B4 expression and increased intracellular calcium levels. In conclusion, large and comprehensive genotyping datasets will be necessary to test the role of rare gene knockouts on complex phenotypes. The transcriptomes and DNA methylomes of erythroblasts show substantial differences correlating with their developmental stages and that may be implicated in HbF production. These results also suggest a strong implication of erythroid enhancers and miRNAs in developmental stage specificity. Finally, characterizing the erythroid-specific enhancer of ATP2B4 suggest that integrating epigenomic, transcriptomic and gene editing experiments can be a powerful approach to characterize non-coding genetic variants. These results implicate ATP2B4 in erythroid cell hydration, which is associated with malaria susceptibility and SCD severity, suggesting that therapies targeting this gene could impact diseases affecting millions of individuals worldwide

Identification of Genetic and Epigenetic Risk Factors for Psoriasis and Psoratic Arthritis

Psoriasis: PS) is a common incurable inflammatory skin disease affecting 2-3% of the European population. ~10-30% of patients develop psoriatic arthritis: PsA). Genetic variation in the major histocompatibility complex: MHC) increases risk of developing PS. However, only ~10% of individuals with this risk factor develop PS, indicating that other genetic effects and environmental triggers are important. In order to identify novel susceptibility genes of PS and PsA, I performed the first large scale genome wide association scan for psoriasis susceptibility loci using 233 cases and 519 controls. It revealed that genes of the immune system and of the barrier are associated with psoriasis. The MHC: psoriasis susceptibility 1 or PSORS1) conferred the strongest risk factor for PS and PsA. The study also confirmed recently identified associations with interleukin-23 receptor and interleukin-12B in both PS and PsA. Novel loci with modest effect were also identified, including a region on chromosome 4q27 that contains genes for interleukin 2 and interleukin 21 that has been implicated in other autoimmune diseases, and seven additional regions that included chromosome 13q13 and 15q21. A follow-up study, aimed to identify potential functional SNPs in the PSORS1 region, implicated an allele-specific repressor role of SNP rs10456057 via binding to nuclear transcriptional factors. Further study with additional PSORS1 SNPs identified enhancer activity of the risk allele of SNP rs13191343 in differentiating keratinocytes, and the presence of the PSORS1 risk allele is correlated with CDSN: corneodesmosin) expression, which would affect skin barrier formation. Finally, this thesis also describes the first genome-wide study of altered CpG methylation in psoriatic skin. The study determined the methylation levels at 27,578 CpG sites in skin samples from individuals with psoriasis: 12 involved, 8 uninvolved) and 10 unaffected individuals. Involved skin differed from normal skin at 1,108 CpG sites at adjusted p-value \u3c 0.05. Twelve of those CpG sites mapped to the epidermal differentiation complex close to genes that are highly up-regulated in psoriasis. Hierarchical clustering of 50 of the top differentially methylated sites accurately separated all psoriatic skin samples: involved and uninvolved) from normal skin. Methylation at 12 CpG sites was significantly correlated with expression levels of a nearby gene. Taken together, the thesis reveals that the genetic and epigenetic risk factors of psoriasis lead to alterations in genes of skin barrier and immune system which act together to trigger the pathogenesis of the disease

Haiguste ja koespetsiifiliste DNA metülatsioonil põhinevate biomarkerite uurimine

Väitekirja elektrooniline versioon ei sisalda publikatsiooneDNA-s sisalduv geneetiline informatsioon annab vajalikud juhised organismi kasvuks ja arenguks. Lisaks DNA nukleotiidsele järjestusele mõjutavad neid protsesse ka DNA-s esinevad modifikatsioonid. Enim uuritud DNA modifikatsioon on DNA metülatsioon, mis tähendab metüülrühma lisamist tsütosiini külge. DNA on tihtilugu metüleeritud regiooniti, moodustades niinimetatud metülatsioonimustreid. Need “mustrid“ osalevad geeniekspressiooni regulatsioonis, lülitades teatud rakkudes geene sisse ja välja või kohandades nende aktiivsust. On oluline märkida, et DNA metülatsioon on tugevalt mõjutatud keskkonnateguritest, nimelt vastavalt keskkonnatingimustele võidakse teatud regioone metüleerida või vastupidi, metüülrühmi eemaldada. Seega on DNA metülatsioon üheks vahelüliks geneetika ja keskkonna vahel. Paljud neist “mustritest“ on omased tavalistele bioloogilistele protsessidele, kuid leidub ka selliseid, mis viitavad haiguse olemasolule. Näiteks on spetsiifilisi metülatsioonimustreid täheldatud diabeedi, neuroloogiliste häirete ja vähi puhul. Seetõttu peetakse neid “mustreid“ ka headeks biomarkeri kandidaatideks, sobides iseloomustama näiteks teatud haiguste kulgu. Käesolev väitekiri keskendubki DNA metülatsiooni uurimisele erinevates kudedes ja seisundites, et leida potentsiaalseid biomarkereid. Selleks kasutati erinevaid bioinformaatika ja statistika meetodeid. Kokku viidi läbi kolm publitseeritud uuringut, mille käigus uuriti nii koe- kui endometrioosispetsiifilisi biomarkeri kandidaate kui ka DNA metülatsiooni muutusi emaka endomeetriumi embrüole vastuvõtlikuks muutumise perioodil. Lisaks arendati doktoritöö raames välja uudne ja kasutajasõbralik veebirakendus – MethSurv, mis kasutades suurprojekti “The Cancer Genome Atlas” (TCGA) andmeid, võimaldab kasutajal uurida vähipatsientide elumust konkreetse DNA metülatsioonil põhineva prognostiliste markeri põhjal.DNA contains the genetic information required for the growth and development of the organism. In addition to the nucleotide sequence, certain chemical modifications influence the activity of the DNA. The most studied DNA modification is DNA methylation, where a methyl group is added to the cytosine base of the DNA. DNA is often methylated within a genomic region, forming so-called “methylation patterns.” These "patterns" are involved in the regulation of gene expression by switching genes in and out of certain cells or adjusting their activity. Environmental factors strongly influence DNA methylation; wherein certain genomic regions may be methylated or unmethylated. Thus, methylation patterns serve as a mediator between the environment and genomes. Many of these "patterns" are inherited in normal biological processes. However, some of these patterns indicate the presence of the disease. For example, specific methylation patterns have been observed in diabetes, neurological disorders, and cancer. Therefore, methylation patterns are considered as biomarker candidates to characterize the progression of certain diseases or normal biological process. This thesis focuses on the study of DNA methylation in different tissues and conditions to identify potential biomarker candidates using various bioinformatics and statistical methods. In total, three studies were included in this thesis to investigate both tissue and endometriosis-specific biomarker candidates as well as changes in DNA methylation during the transition from pre-receptive to the receptive state of the endometrium. In addition, a novel and user-friendly web application MethSurv was developed in this thesis. MethSurv uses methylation and clinical data from the publicly available “The Cancer Genome Atlas” (TCGA). The MethSurv tool is aimed at assisting the scientific community in exploring methylation-based prognostic biomarkers.https://www.ester.ee/record=b522744

Aberrant methylation patterns in colorectal cancer: A meta-analysis

Colorectal cancer is among the leading causes of cancer death worldwide. Despite numerous molecular characterizations of the phenomenon, the exact dynamics of its onset and progression remain elusive. Colorectal cancer onset has been characterized by changes in DNA methylation profiles, that, owing to the stability of their patterns, are promising candidates to shed light on the molecular events laying at the base of this phenomenon. To exploit this stability and reinforce it, we conducted a meta-analysis on publicly available DNA methylation datasets generated on: normal colorectal, adenoma (ADE) and adenocarcinoma (CRC) samples using the Illumina 450k array, in the systems medicine frame, searching for tumor gene episignatures, to produce a carefully selected list of potential drivers, markers and targets of the disease. The analysis proceeds from a differential meta-analysis of the methylation profiles using an analytical pipeline recently developed by our group [1], through network reconstruction, topological and functional analyses, to finally highlight relevant epigenomic features. Our results show that genes already highlighted for their genetic or transcriptional alteration in colorectal cancer are also differentially methylated, reinforcing -regardless of the level of cellular control- their role in the complex of alterations involved in tumorigenesis. These findings were finally validated in an independent cohort from The Cancer Genome Atlas (TCGA)

Origin of Maternal Age Effect in Congenital Heart Disease Risk for Offspring

Increasing maternal age is widely acknowledged to lead to greater likelihood of pregnancy complications and congenital abnormalities, but the basis of this effect has not been well studied. Often dismissed as the product of oocyte ageing, the mechanistic basis of this maternal age effect is likely more complex. Congenital heart disease is a classic complex disease with multiple genetic and environmental modifiers, including maternal age. Maternal ageing is a known risk-factor in humans, and has been shown to exist in an Nkx2-5 haploinsufficient mouse model for the disease. This mouse model\u27s maternal age risk is dependent upon strain background, with C57BL/6N pure line and FVB/N x C57BL/6N F2 intercross pups being at risk due to maternal ageing, and A/J x C57BL/6N F2 intercrosses showing no maternal age risk. This indicates a maternal genetic component to maternal age risk, and implies that though ageing is inevitable, the negative effects on offspring are not. Using this model, this study examines whether the maternal age effect is due to oocyte ageing or a maternally intrinsic factor, shows a remediating treatment for maternal age risk, and defines epigenetic changes in offspring resulting from maternal ageing. Reciprocal ovarian transplants between old and young FVB/N x C57BL/6N F1 mothers were used to localize the basis of the maternal age effect to the mother. In spite of ovulating from ovaries aged well beyond the mouse\u27s normal reproductive life span, young mothers were at no higher risk for ventricular septal defects (VSD), while old mothers showed a persistent high risk for VSD in spite of ovulating young oocytes. Voluntary exercise experiments where FVB/N x C57BL/6N F1 mothers were given access to running wheels over the course of their lifetime showed that exercise decreased maternal age risk to levels indistinguishable from that of young mothers. Additionally, late-onset exercise was shown to be effective at reducing maternal age risk after just three months\u27 exposure, even with no overt changes in body mass, composition, or glucose tolerance. To study the impact of maternal ageing on epigenetic profiles, reduced representation bisulfite sequencing was used to compare aged and young sedentary fetal hearts and aged exercise fetal heart tissue. These comparisons showed eight differentially methylated regions, altered by maternal ageing but recovered by exercise treatment. These studies are conclusive proof that nonsyndromic maternal age risk is not due to oocyte ageing, but instead to a modifiable, maternally intrinsic risk factor. These studies also suggest the possibility of exercise as a prescription to prevent or turn back maternal age\u27s negative impacts. Exercise as an intervention poses tempting possibilities as a safe intervention for at-risk populations. Further investigation into the mechanistic influence of epigenetics in this effect may identify risk biomarkers for testing in maternal populations, and may provide keys to the underlying genetic architecture for congenital malformations such as congenital heart disease

A Systems Biology Approach to Investigating Host-Pathogen Interactions in Infection with Burkholderia pseudomallei

This thesis applies systems approaches in order better to understand host-pathogen interactions in infectious diseases; it focuses on the intracellular bacterium Burkholderia pseudomallei, the causative agent of the human disease melioidosis. Little is known about the epigenetic changes in host cells during infection. This study assesses genome-wide patterns of the epigenetic marker DNA methylation in host cells following infection with B. pseudomallei. The studies of this thesis concern the infection of human macrophage-like U937 cells with B. pseudomallei and the DNA methylation levels were measured during the early stages of infection. Analyses reveal significant changes in infected cells (compared to uninfected controls) at multiple locations in the host DNA. Most of the methylation changes in infected cells are losses rather than gains in methylation. Five different differential methylation patterns (constant, early, late, transient, and oscillatory) are identified. Differentially methylated sites mapped to genes that may affect virulence, e.g. genes involved in actin regulation, immune response, inflammatory response, and nitric oxide generation. The thesis also measures whole blood DNA methylation profiles of patients diagnosed with melioidosis in order to test the potential role of host DNA methylation in melioidosis. The results demonstrate that patients with melioidosis are separated from healthy subjects by their distinct methylation profiles. The differentially methylated regions reported here can potentially be used as biomarkers for classification and prognostication of infectious diseases. In addition to exploring the changes to the host, a comprehensive understanding of the pathogen interference and the search for countermeasures requires a framework that assesses how the host changes the pathogen metabolically. In this thesis, to understand the role of trehalose pathway in virulence, computational models were constructed by integrating kinetic information, genomics data and literature surveys. Existing kinetic models of the trehalose pathway were implemented and extended allowing for the in silico investigation of the trehalose mutant. Further, metabolic networks of B. pseudomallei were analysed at the genome scale to identify molecular links between trehalose and metabolic pathways such as glycolysis. The genome- scale reconstruction of the B. pseudomallei metabolic network was used to simulate growth under different conditions and predict the effects of gene knockouts. This thesis not only expands the existing knowledge about B. pseudomallei infection, the novel approaches employed here will stimulate a wider understanding of the applications of systems biology to host-pathogen research and defence needs

Regulators Associated with Clinical Outcomes Revealed by Dna Methylation Data in Breast Cancer

The regulatory architecture of breast cancer is extraordinarily complex and gene misregulation can occur at many levels, with transcriptional malfunction being a major cause. This dysfunctional process typically involves additional regulatory modulators including DNA methylation. Thus, the interplay between transcription factor (TF) binding and DNA methylation are two components of a cancer regulatory interactome presumed to display correlated signals. As proof of concept, we performed a systematic motif-based in silico analysis to infer all potential TFs that are involved in breast cancer prognosis through an association with DNA methylation changes. Using breast cancer DNA methylation and clinical data derived from The Cancer Genome Atlas (TCGA), we carried out a systematic inference of TFs whose misregulation underlie different clinical subtypes of breast cancer. Our analysis identified TFs known to be associated with clinical outcomes of p53 and ER (estrogen receptor) subtypes of breast cancer, while also predicting new TFs that may also be involved. Furthermore, our results suggest that misregulation in breast cancer can be caused by the binding of alternative factors to the binding sites of TFs whose activity has been ablated. Overall, this study provides a comprehensive analysis that links DNA methylation to TF binding to patient prognosis