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Research on the interactions between microbes and polymeric materials constitutes an important part in antimicrobial identification and provides an insight into microbial response on the polymer surfaces. Herein, a high-content screening method with polymer microarray technology was developed to investigate microbe-polymer interactions, especially in studying adhesion/repellence of microbes (bacteria and parasites). Firstly, the polymer microarray approach was used to successfully identify polymers which either selectively captured or prevented the binding of major food-borne pathogen, Salmonella Typhimurium. A parallel study with a lab strain of Escherichia coli was also carried out, revealing polymers which either displayed a common binding activity or which exhibited species discrimination. Likewise, this polymer microarray technology was applied to more bacterial strains, such as Campylobacter, Clostridium, Streptococcus, Klebsiella and their cocktails to discover families of substrates that displayed strong broad-spectrum bacterial non-binding activity. These synthetic polymers represented a novel class of coating materials which can be used to prevent surface colonisation and subsequent formation of bacterial biofilms. The study of protozoan-polymer interactions was also explored in this thesis. Polymers were identified which either bound or prevented parasites (Crysporidium parvum and Giardia lamblia) binding. Material properties, including wettability, surface roughness and polymer composition were analysed to study correlation of parasite binding and the generation of polymer structure function relationships.

Ambystoma tigrinum undergoes an obligatory metamorphosis while A. mexicanum fails to metamorphose and exhibits paedomorphosis. While it is clear that salamander paedomorphosis is associated with genetic changes that delay developmental timing, it is not clear when and how these changes manifest during development. It is possible that paedomorphic and metamorphic larvae show equivalent patterns of developmental until late in the larval period, when brain regions become competent to stimulate the release of metamorphic hormones. To test this hypothesis, I compared gene expression patterns between the brains of A. mexicanum and A. t. tigrinum larvae. In support of the developmental equivalence hypothesis, 114 differentially expressed genes (DEGs) were identified in common between the species and all but 2 showed the same temporal pattern of expression. However, more DEGs were identified uniquely from each species. In particular, several genes that are associated with the hypothalamus-pituitaryinterrenal axis, which is implicated in metamorphic regulation in amphibians, exhibited significant expression differences between A. mexicanum and A. t. tigrinum larvae. The results show that metamorphic and paedomorphic modes of development are associated with different transcriptional programs in the brain and these programs diverge during early larval development.

The emerging field of personalized medicine and the prediction of side effects experienced due to pharmaceutical drugs is being studied intensively in the post genomic era. The molecular basis of inheritance and disease susceptibility is being unravelled, especially through the use of rapidly evolving new technologies. This in turn facilitates analyses of individual variations in the whole genome of both single subjects and large groups of subjects. Genetic variation is a common occurrence and although most genetic variations do not have any apparent effect on the gene product some do exhibit effects, such as an altered ability to detoxify xenobiotics. The human body has a highly effective detoxification system that detoxifies and excretes endogenous as well as exogenous toxins. Numerous studies have proved that specific genetic variations have an influence on the efficacy of the metabolism of pharmaceutical drugs and consequently the dosage administered. The primary aim of this project was the local implementation and assessment of two different genotyping approaches namely: the Applied Biosystems SNaPshot technique and Affymetrix DMET microarray. A secondary aim was to investigate if links could be found between the genetic data and the biochemical detoxification profile of participants. I investigated the approaches and gained insight into which method would be better for specific local applications, taking into consideration the robustness and ease of implementation as well as cost effectiveness in terms of data generated. The final study cohort comprised of 18 participants whose detoxification profiles were known. Genotyping was performed using the DMET microarray and SNaPshot techniques. The SNaPshot technique was used to genotype 11 SNPs relating to DNA repair and detoxification and was performed locally. Each DMET microarray delivers significantly more data in that it genotypes 1931 genetic markers relating to drug metabolism and transport. Due to the absence of a local service supplier, the DMET - microarrays were outsourced to DNALink in South Korea. DNALink generated raw data which was analysed locally. I experienced many problems with the implementation of the SNaPshot technique. Numerous avenues of troubleshooting were explored with varying degrees of success. I concluded that SNaPshot technology is not the best suited approach for genotyping. Data obtained from the DMET microarray was fed into the DMET console software to obtain genotypes and subsequently analysed with the help of the NWU statistical consultation services. Two approaches were followed: firstly, clustering the data and, secondly, a targeted gene approach. Neither of the two methods was able to establish a relationship between the DMET genotyping data and the detoxification profiling. For future studies to successfully correlate SNPs or SNP groups and a specific detoxification profile, two key issues should be addressed: i) The procedure for determining the detoxification profile following substrate loading should be further refined by more frequent sampling after substrate loading. ii) The number of participants should be increased to provide statistical power that will enable a true representation of the particular genetic markers in the specific population. The statistical analyses, such as latent class analyses to cluster the participants will also be of much more use for data analyses and interpretation if the study is not underpowered. / Thesis (M.Sc. (Biochemistry))--North-West University, Potchefstroom Campus, 2011.

The emerging field of personalized medicine and the prediction of side effects experienced due to pharmaceutical drugs is being studied intensively in the post genomic era. The molecular basis of inheritance and disease susceptibility is being unravelled, especially through the use of rapidly evolving new technologies. This in turn facilitates analyses of individual variations in the whole genome of both single subjects and large groups of subjects. Genetic variation is a common occurrence and although most genetic variations do not have any apparent effect on the gene product some do exhibit effects, such as an altered ability to detoxify xenobiotics. The human body has a highly effective detoxification system that detoxifies and excretes endogenous as well as exogenous toxins. Numerous studies have proved that specific genetic variations have an influence on the efficacy of the metabolism of pharmaceutical drugs and consequently the dosage administered. The primary aim of this project was the local implementation and assessment of two different genotyping approaches namely: the Applied Biosystems SNaPshot technique and Affymetrix DMET microarray. A secondary aim was to investigate if links could be found between the genetic data and the biochemical detoxification profile of participants. I investigated the approaches and gained insight into which method would be better for specific local applications, taking into consideration the robustness and ease of implementation as well as cost effectiveness in terms of data generated. The final study cohort comprised of 18 participants whose detoxification profiles were known. Genotyping was performed using the DMET microarray and SNaPshot techniques. The SNaPshot technique was used to genotype 11 SNPs relating to DNA repair and detoxification and was performed locally. Each DMET microarray delivers significantly more data in that it genotypes 1931 genetic markers relating to drug metabolism and transport. Due to the absence of a local service supplier, the DMET - microarrays were outsourced to DNALink in South Korea. DNALink generated raw data which was analysed locally. I experienced many problems with the implementation of the SNaPshot technique. Numerous avenues of troubleshooting were explored with varying degrees of success. I concluded that SNaPshot technology is not the best suited approach for genotyping. Data obtained from the DMET microarray was fed into the DMET console software to obtain genotypes and subsequently analysed with the help of the NWU statistical consultation services. Two approaches were followed: firstly, clustering the data and, secondly, a targeted gene approach. Neither of the two methods was able to establish a relationship between the DMET genotyping data and the detoxification profiling. For future studies to successfully correlate SNPs or SNP groups and a specific detoxification profile, two key issues should be addressed: i) The procedure for determining the detoxification profile following substrate loading should be further refined by more frequent sampling after substrate loading. ii) The number of participants should be increased to provide statistical power that will enable a true representation of the particular genetic markers in the specific population. The statistical analyses, such as latent class analyses to cluster the participants will also be of much more use for data analyses and interpretation if the study is not underpowered. / Thesis (M.Sc. (Biochemistry))--North-West University, Potchefstroom Campus, 2011.

<p>The translational potential of diagnostic and prognostic platforms developed using expression microarray technology is evident. However, the majority of array-based diagnostics have yet to make their way into the clinical laboratory. Current approaches tend to focus on development of multi-gene classifiers of disease subtypes, but very few studies evaluate the translational potential of these assays. Likewise, only a handful of studies focus on development of approaches to optimize array-based tests for the ultimate goal of clinical utility. Prior to translation into the clinical setting, molecular diagnostic platforms should demonstrate a number of characteristics to ensure optimal and efficient testing and patient care. Assays should be accurate and precise, technically and biologically robust, and should take into account normal sources of biological variance that could ultimately affect test results. The overarching goal of the research presented in this dissertation is to develop methods for evaluating and optimizing the translational potential of molecular diagnostics developed using expression microarray technology.</p><p> </p><p>The first research section of this dissertation is focused on our evaluation of the impact of intratumor heterogeneity on precision in microarray-based assays in breast cancer. We conducted genome-wide expression profiling on 50 needle core biopsies from 18 breast cancer patients. Global profiles of expression were characterized using unsupervised clustering methods and variance components models. Array-based measures of estrogen (ER) and progesterone receptor (PR) status were compared to immunohistochemistry. The precision of genomic predictors of ER pathway status, recurrence risk, and sensitivity to chemotherapeutics were evaluated by interclass correlation. Results demonstrated that intratumor variation was substantially less than the total variation observed across the patient population. Nevertheless, a fraction of genes exhibited significant intratumor heterogeneity in expression. A high degree of reproducibility was observed in single gene predictors of ER (intraclass correlation coefficient (ICC)=0.94) and PR expression (ICC=0.90), and in a multi-gene predictor of ER pathway activation (ICC=0.98) with high concordance with immunohistochemistry. Substantial agreement was also observed for multi-gene signatures of cancer recurrence (ICC=0.71), and chemotherapeutic sensitivity (ICC=0.72 and 0.64). Together, these results demonstrated that intratumor heterogeneity, although present at the level of individual gene expression, does not preclude precise micro-array based predictions of tumor behavior or clinical outcome in breast cancer patients.</p><p> </p><p>Leading into the second research section, we observed that in some cancer types, certain genes behave as molecular switches and have either an "on" or "off" expression state. Specifically, we observed these molecular switch genes exist in breast cancer as robust diagnostic and prognostic markers, including ER, PR, and HER2, and define tumor subtypes associated with different treatment and patient survival. We hypothesized that clinically relevant molecular switch (bimodal) genes exist in epithelial ovarian cancer, a type of cancer with no established molecular subgroups. To test this hypothesis, we applied a bimodal discovery algorithm to a publically available ovarian cancer expression microarray dataset (GSE9891:285 tumors; 246 malignant serous (MS), 20 endometrioid (EM), 18 low malignant potential (LMP) ovarian carcinomas). Genes with robust bimodal expression were identified across all ovarian tumor types and within selected subtypes. Of these bimodal genes, 73 demonstrated differential expression between LMP vs. MS and EM, and 22 genes distinguished MS from EM. Fourteen bimodal genes had significant association with survival among MS tumors. When these genes were combined into a single survival score, the median survival for patients with a favorable versus unfavorable score was 65 versus 29 months (p<0.0001, HR=0.4221). Two independent datasets (n=53 high grade, advanced stage serous and n=119 advanced stage ovarian tumors) validated the survival score performance. Taken together, the results of this study revealed that genes with bimodal expression patterns not only define clinically relevant molecular subtypes of ovarian carcinoma, but also provide ideal targets for translation into the clinical laboratory.</p><p> </p><p>Finally, the third research section of this dissertation focuses on development of robust blood-based molecular markers of decompression stress (DS). DS is defined as the pathophysiological response to inert gas coming out of solution in the blood and tissues when a body experiences a reduction in ambient pressure. To date, there are no established molecular markers of DS. We hypothesized that comparing gene expression before and after human decompression exposures by genome-wide expression profiling would identify candidate molecular markers of DS. Peripheral blood was collected 1hr before and 2hr after 93 hyperoxic, heliox experimental dives (n=54). Control arms included samples collected 1 hour before and 2 hours after high pressure oxygen breathing (n= 9) and surface exercise (n=9), and samples collected at 7am and 5pm for time of day (n=11). Pre and post-dive expression data collected from normoxic nitrox experimental dives were utilized for independent validation. Blood samples were collected into PaxGene RNA tubes. RNA was extracted and processed for globin reduction prior to cDNA synthesis and Affymetrix U133A GeneChip hybridization. 746 genes were differentially expressed following hyperoxic, heliox decompression exposures (permutation adjusted p-value cutoff 1.0E-4). After filtering control significant genes, 726 genes remained. Pathway analysis demonstrated a significant portion of genes were associated with innate immune response (p<0.0001). A 362 multi-gene signature of significant, covariant genes was then applied to the independent dataset and demonstrated differentiation between pre and post-dive samples (p=0.0058). There was no significant correlation between signature and venous bubble grade or bottom time in the validation study. Our results showed that expression profiling of peripheral blood following decompression exposures, while controlling for experimental and normal sources of biological variance, identifies a reproducible multi-gene signature of differentially expressed genes, primarily comprising genes associated with innate immune response and independent of venous bubble grade or dive profile. </p><p> </p><p>Taken together, the research and results presented in this dissertation represent considerable advances in the development of approaches to guide microarray-based diagnostics towards the ultimate goal of clinical translation.</p> / Dissertation

Previous studies of the grey wolf (Canis lupus) using microsatellites have showed strong population structure despite the high mobility of individuals. I re-assessed the structure of North American grey wolves by genotyping 132 wolves at a genome-wide set of >26 000 single nucleotide polymorphisms (SNPs), and found less population structure, a strong pattern of isolation by distance, and determined that gene flow between subpopulations relates to prey specialization. To assess how accurately smaller data sets assign individuals, I analyzed sub-sets of SNPs and found that small marker sets varied greatly in estimates of subpopulation assignment, and showed high discordance with assignments determined when using all 26k markers. Finally, using a genome scan to detect natural selection I identified SNPs in three genes that may have undergone directional selection, contain variation with observed phenotypic consequences in other mammal species and may be related to adaptation in grey wolves. / Systematics and Evolution

El hierro es un oligoelemento esencial para la supervivencia celular ya que es un cofactor de muchas enzimas y forma parte de la estructura de muchas proteínas. Es por este motivo que los microorganismos necesitan mecanismos eficientes para la captación de hierro cuándo carecen del mismo. Está ampliamente descrito que la captación de hierro es uno de los pasos clave en el desarrollo de un patógeno dentro de su huésped 1. Pero, aunque el Fe es indispensable, su exceso en el citoplasma es tóxico para la célula ya que cataliza la reacción de Fenton con la consiguiente formación de radicales hidroxilo 2, 3. Por todo ello la homeóstasis del Fe2+ se encuentra estrictamente controlada. La proteína Fur (ferric uptake regulator) es el principal regulador transcripcional implicado en la respuesta celular a la concentración de hierro, controlando tanto la inducción de sistemas de captación de Fe2+ de alta afinidad, como la expresión de proteínas para el almacenamiento y enzimas que utilizan hierro 4. Normalmente Fur, asociado al ión Fe2+, se une a una secuencia concreta denominada caja Fur presente en la región promotora de los genes que regula, bloqueando de esta manera su transcripción 5. Aunque también puede activar la expresión de diferentes genes de forma directa o indirecta 1. En el presente trabajo se estudia, mediante microarrays de DNA, el papel de la proteína Fur en el patógeno intracelular S. enterica serovar Typhimurium y la relación de este regulador con los mecanismos de virulencia de dicho microorganismo. / Iron is an essential trace element for the cell since it is a cofactor for many enzymes and is a part of the structure of many proteins. It is for this reason that the microorganisms need efficient mechanisms for iron uptake when lacking it. It is widely reported that iron uptake is one of the key steps in the development of a pathogen within its host 1. Although Fe2+ is indispensable, its excess in the cytoplasm is toxic to the cell since it catalyzes the Fenton reaction leading to the formation of hydroxyl radicals 2, 3. Therefore Fe2+ homeostasis is strictly controlled. Protein Fur (ferric uptake regulator) is the major transcriptional regulator involved in the cellular response to iron concentration, by controlling the induction of Fe2+ high affinity uptake systrems and the protein expression of iron storing and utilizing enzymes 4. Normally, Fur, associated to the Fe2+ ion, binds to a specific sequence called Fur box present in the promoter region of genes the regulated genes, thus blocking its transcription 5. Fur also can activate the expression of different genes directly or indirectly 1. The role of the Fur protein in the intracellular pathogen S. enterica serovar Typhimurium and its relationship with the virulence mechanisms of this microorganism is studied in this work by DNA microarrays.

Early detection is one of the most important factors for successful treatment of cancer. Currently, scientists are searching for molecular markers that can help identify and predict outcome and chance of recurrence in patients. In this study, we demonstratet he potential impact of Y-Box binding protein-1 (YB-1) as a marker of aggressiveness and cancer recurrence in breast malignancies by screening one of the largest tissue microarrays in North America. YB-1 is an oncogenic transcription/translation factor, which is over-expressed in the majority of malignancies, including breast cancer. In the cohort of 4049 primary breast tumours, we show that YB-1 is a strong marker of aggressiveness, poor survival and cancer recurrence in all subtypes of human breast cancer with a particularly high frequency of expression in the ER negative basal-like and HER-2 breast cancer subtypes. This suggests that targeting YB-1 may provide a new avenue for therapeutic intervention in these breast cancers that are currently challenging to treat. Cox regression multivariate analysis indicates that YB-1 is second only to nodal status as a strong independent prognostic marker for poor outcome and relapse compared to established clinico-pathological biomarkers, including tumour size, age, grade, ER and HER-2 status. This finding suggests that YB-1 has great potential to be in a priority list of biomarkers for identifying the patients with a higher risk of relapse and poor outcome. Subsequently, we find an association between YB-1 and urokinase Plasminogen Activator (uPA) expression in the basal-like subtype. We then show that YB-1 is involved in the regulation of uPA expression. More importantly, silencing YB-1 or uPA results in a significant reduction in cancer cell invasion. As there are no commercially available YB-linibitors we examine the efficacy of BMS-536924, a small molecule inhibitor for activated IGF-1R/IR on SUM149 cells. We demonstrate that activated IGF-1R is associated with poor survival in primary breast tumours and, that BMS-536924 reduces uPA expression through inhibition YB-1 in SUM149 cells. We therefore conclude that YB-1 is a bio-marker for poor survival and relapse. We also indicate that YB-1 has potential use as a molecular marker in a clinical setting. Inhibiting YB-1 may provide an ideal opportunity for targeted therapy in breast cancer.

[Abstract]Microarray data classification is used primarily to predict unseen data using a model built on categorized existing Microarray data. One of the major challenges is that Microarray data contains a large number of genes with a small number of samples. This high dimensionality problem has prevented many existing classification methods from directly dealing with this type of data. Moreover, the small number of samples increases the overfitting problem of Classification, as a result leading to lower accuracy classification performance. Another major challenge is that of the uncertainty of Microarraydata quality. Microarray data contains various levels of noise and quite often high levels of noise, and these data lead to unreliable and low accuracy analysis as well as the high dimensionality problem. Most current classification methods are not robust enough to handle these type of data properly.In our research, accuracy and noise resistance or robustness issues are focused on. Our approach is to design a robust classification method for Microarray data classification.An algorithm, called diversified multiple decision trees (DMDT) is proposed, which makes use of a set of unique trees in the decision committee. The DMDT method has increased the diversity of ensemble committees andtherefore the accuracy performance has been enhanced by avoiding overlapping genes among alternative trees.Some strategies to eliminate noisy data have been looked at. Our method ensures no overlapping genes among alternative trees in an ensemble committee, so a noise gene included in the ensemble committee can affect onetree only; other trees in the committee are not affected at all. This design increases the robustness of Microarray classification in terms of resistance to noise data, and therefore reduces the instability caused by overlapping genes in current ensemble methods.The effectiveness of gene selection methods for improving the performance of Microarray classification methods are also discussed.We conclude that the proposed method DMDT substantially outperforms the other well-known ensemble methods, such as Bagging, Boosting and Random Forests, in terms of accuracy and robustness performance. DMDT is more tolerant to noise than Cascading-and-Sharing trees (CS4), particularywith increasing levels of noise in the data. The results also indicate that some classification methods are insensitive to gene selection while some methodsdepend on particular gene selection methods to improve their performance of classification.