Viruses have an estimated global population size of 10 to the 31st, with a significant proportion found in the marine environment. Viral lysis of bacteria affects the flow of carbon in the marine microbial food web, but the effects of lysogeny on marine microbial ecology are largely unknown. In this thesis, factors that influence the control of lysogeny were studied in both the phiHSIC/Listonella pelagia phage-host system and in bacterioplankton populations in the Gulf of Mexico. Using macroarrays dotted with phiHSIC amplicons, viral gene expression over the course of a synchronous infection experiment was measured. Early, middle, late, and continually expressed genes were identified, and included open reading frames 45, 28, 18 and 17, respectively. Viral gene expression in cultures of the HSIC-1a pseudolysogen grown in low and normal salinity media was also analyzed. Overall, levels of viral gene expression were higher in the 39 ppt treatment as compared to the 11 ppt tre atment for most ORFs. In the 11 ppt treatment, free phage concentrations were one to two orders of magnitude lower than the 39 ppt treatment while intracellular phage concentrations were one-fold lower. Therefore, at low salinities, expression of phiHSIC genes is repressed resulting in a lysogenic-like state, while at 39 ppt, lytic interactions dominated. Few viral genes were highly expressed at low salinity, suggesting that repression of viral genes was controlled by host genes. Samples from the eutrophic Mississippi River Plume and the oligotrophic Gulf of Mexico were analyzed for lytic phage production and occurrence of lysogeny. Significant lytic viral production was only observed three stations, none of which were located within the MRP. This signifies that system productivity is not an accurate predictor of viral productivity. The lysogenic fraction was also inversely correlated to bacterial activity, which decreased with depth. These findings support the hypothesis that lysogeny is a survival mechanism for phages when host cell density is low or when conditions do not favor growth. A unifying theme from these experiments was that lytic processes dominated when bacterial growth conditions were optimal, while lysogeny was observed at unfavorable growth conditions or environmental stress (low salinity).

Capturing the mechanism of gene regulation in a living cell is essential to predict the behavior of cell in response to intercellular or extra cellular factors. Such prediction capability can potentially lead to development of improved diagnostic tests and therapeutics [21]. Amongst reverse engineering approaches that aim to model gene regulation are Dynamic Bayesian Networks (DBNs). DBNs are of particular interest as these models are capable of discovering the causal relationships between genes while dealing with noisy gene expression data. At the same time, the problem of discovering the optimum DBN model, makes structure learning of DBN a challenging topic. This is mainly due to the high dimensionality of the search space of gene expression data that makes exhaustive search strategies for identifying the best DBN structure, not practical. In this work, for the first time the application of a covariance-based evolutionary search algorithm is proposed for structure learning of DBNs. In addition, the convergence time of the proposed algorithm is improved compared to the previously reported covariance-based evolutionary search approaches. This is achieved by keeping a fixed number of good sample solutions from previous iterations. Finally, the proposed approach, M-CMA-ES, unlike gradient-based methods has a high probability to converge to a global optimum. To assess how efficient this approach works, a temporal synthetic dataset is developed. The proposed approach is then applied to this dataset as well as Brainsim dataset, a well known simulated temporal gene expression data [58]. The results indicate that the proposed method is quite efficient in reconstructing the networks in both the synthetic and Brainsim datasets. Furthermore, it outperforms other algorithms in terms of both the predicted structure accuracy and the mean square error of the reconstructed time series of gene expression data. For validation purposes, the proposed approach is also applied to a biological dataset composed of 14 cell-cycle regulated genes in yeast Saccharomyces Cerevisiae. Considering the KEGG1 pathway as the target network, the efficiency of the proposed reverse engineering approach significantly improves on the results of two previous studies of yeast cell cycle data in terms of capturing the correct interactions. / Thesis (Master, Computing) -- Queen's University, 2008-09-09 11:35:33.312

In order to derive reliable protein networks it has recently been suggested that the combination of information from both gene and protein level is required. In this thesis a combination of gene expression and protein chip analysis was performed when constructing protein networks. Proteins with high affinity to the same substrates and encoded by genes with high correlation is here thought to constitute reliable protein networks. The protein networks derived are unfortunately not as reliable as were hoped for. According to the tests performed, the method derived in this thesis does not perform more than slightly better than chance. However, the poor results can depend on the data used, since mismatching and shortage of data has been evident.

The metabolic syndrome is a cluster of metabolic risk factors such as diabetes type II, dyslipidemia, hypertension, obesity, microalbuminurea and insulin resistance, which in the recent years has increased greatly in many parts of the world. In this thesis decision trees were applied to the BioExpress database, including both clinical data about donors and gene expression data, to investigate nuclear receptors ability to serve as markers for the metabolic syndrome. Decision trees were created and the classification performance for each individual risk factor were then analysed. The rules generated from the risk factor trees were compared in order to search for similarities and dissimilarities. The comparisons of rules were performed in pairs of risk factors, in groups of three and on all risk factors and they resulted in the discovery of a set of genes where the most interesting were the Peroxisome Proliferator Activated Receptor - Alpha, the Peroxisome Proliferator Activated Receptor - Gamma and the Glucocorticoid Receptor. These genes existed in pathways associated with the metabolic syndrome and in the recent scientific literature.

Recent methods to infer genetic networks are based on identifying gene interactions by similarities in expression profiles. These methods are founded on the assumption that interacting genes share higher similarities in their expression profiles than non-interacting genes. In this dissertation this assumption is validated when using mutual information as a similarity measure. Three algorithms that calculate mutual information between expression data are developed: 1) a basic approach implemented with the histogram technique; 2) an extension of the basic approach that takes into consideration time delay between expression profiles; 3) an extension of the basic approach that takes into consideration that genes are regulated in a complex manner by multiple genes. In our experiments we compare the mutual information distributions for profiles of interacting and non-interacting genes. The results show that interacting genes do not share higher mutual information in their expression profiles than non-interacting genes, thus contradicting the basic assumption that similarity measures need to fulfil. This indicates that mutual information is not appropriate as similarity measure, which contradicts earlier proposals.

The central question investigated in this project was whether clustering of gene expression patterns could be done more biologically accurate by providing the clustering technique with additional information about the genes as input besides the expression levels. With the term biologically accurate we mean that the genes should not only be clustered together according to their similarities in expression profiles, but also according to their functional similarity in terms of functional annotation and metabolic pathway. The data was collected at AstraZeneca R&D Mölndal Sweden and the applied computational technique was self-organising maps. In our experiments we used the combination of expression profiles together with enzyme classification annotation as input for the self-organising maps instead of just the expression profiles. The results were evaluated both statistically and biologically. The statistical evaluation showed that our method resulted in a small decrease in terms of compactness and isolation. The biological evaluation showed that our method resulted in clusters with greater functional homogeneity with respect to enzyme classification, functional hierarchy and metabolic pathway annotation.

High stromal PDGF receptor B expression was shown to have strong prognostic value in a studyinvolving over 600 breast cancer patients however, the molecular role of the receptor in tumordevelopment remains unclear. In this project we studied the spatial distribution and expressionlevels of a panel genes and markers associated with PDGF signaling, in breast cancer tumormicroenvironment (TME) using a newly developed technique -in situ sequencing. The techniquerelies on padlock probes which we validated with corresponding RNA sequencing, microarray,and immunohistochemistry data. Our results showed that high PDGF receptor B mRNA colocalizedwith markers of two pathways, TGFβ and Hedgehog signaling; this suggests that theymight contribute to the PDGF-receptor B-driven tumor growth. We also showed that stromalPDGF signaling is stimulated predominantly by tumor cells. Finally, further expression profilingof each individual gene revealed that CXCL14 was mainly expressed in the stroma, ACTA2expression was enriched in the tumor/stroma boundary while the stem-cell marker, OCT3, wasexpressed in the interior of the tumor cells.

Breast cancer is the most common tumor disease diagnosed in women worldwide. The hereditary character of this disease is observed in 5-10 % of all cases, and it is usually caused by a pathogenic mutation in one of the predisposition genes. Although a variety of pathogenic mutations in the coding sequences of these genes was described, the cause of the disease is still unknown in many familial cases (> 50%). A great number of identified pathogenic mutations were localized in the consensus splicing sites, which results in the formation of aberrant mRNA splicing variants and their damaged protein isoforms. However, little is known about mutations affecting regulatory splicing sites, which can result in the translation of similarly affected mRNAs. In this work, we proposed a method for indirect detection of mutations affecting the natural splicing pattern of any gene of our interest based on multiplex PCR and NGS with high sensitivity. Verification of this method on the BRCA1 model gene revealed the presence of the total of 94 splicing variants in peripheral leucocytes and healthy breast and adjacent fat tissues. This is the most detailed catalogue of physically occurring BRCA1 mRNA variants thus far. The most commonly occurring variants, maintaining open reading frame, were quantified by RT-qPCR which...

The infection of plants by Heterodera glycines, commonly known as soybean cyst nematode (SCN), is a serious agricultural problem of worldwide extent. Meanwhile, it provides an excellent experimental model to study basic aspects of how cells function, in particular, during biotic challenge. Heterodera glycines challenges plant cells by initiating, developing and sustaining an interaction that results in the formation of a nurse cell from which the nematode derives nourishment. The presented experiments examine (1) how a cell can be de-differentiated and reprogrammed to perform a much different biological role and (2) how a cell’s immune responses can be engaged or suppressed to accomplish that goal. The observation of alpha soluble N-ethylmaleimide-sensitive factor attachment protein (alpha-SNAP) expression, its location within the rhg1 locus and known involvement in the vesicular transport machinery relating to defense made it a strong candidate for further functional analysis. Functional studies demonstrated that overexpression of alpha-SNAP in the susceptible G. max[Williams 82/PI 518671] genotype that lacks its expression results in the partial suppression of H. glycines infection. This indicated that the vesicles could be delivering cargo to the site of infection to engage a defense response. High levels of expression of a cell wall modifying gene called xyloglucan endotransglycosylase also occur during defense. XTHs associate with vesicles, act in the apoplast outside of the cell, and have a well-known function in cell wall restructuring. These observations indicated that alterations in the cell wall composition of nurse cells could be important for the successful defense response. Overexpression of a G. max xyloglucan endotransglycosylase (Gm-XTH) in the susceptible G. max[Williams 82/PI 518671] genotype resulted in a significant negative effect on H. glycines as well as R. reniformis parasitism. The results, including preliminary experiments on components of the vesicle transport system, identify a potent mechanism employed by plants to defend themselves from two types of plant-parasitic nematodes.

<p>Through a number of biological, technological and computational achievements during the 20th century and the devoted work of hundreds of researchers the sequence of the human and other genomes are now available in public databases. The current challenge is to begin to understand the information encoded by the DNA sequence, to elucidate the functions of the proteins and RNA molecules encoded by the genes as well as how they are regulated. For this purpose new technologies within the area of functional genomics are being developed. Among those are powerful tools for gene expression analysis, such as microarrays, providing means to investigate when and where certain genes are used.</p><p>This thesis describes a method that was developed to enable gene expression analysis, on the transcriptome level, in small tissue samples. It relies on PCR amplification of the 3’-ends of cDNA (denoted 3’-end signature tags). PCR is a powerful technology for amplification of nucleic acids, but has not been used much for transcript profiling since it is generally considered to introduce biases, distorting the original relative transcript levels. The described method addresses this issue by generating uniformly sized representatives of the transcripts/cDNAs prior to amplification. This is achieved through sonication which, unlike restriction enzymes, does not require a specific recognition sequence and fragments each transcript randomly. The method was evaluated using cDNA microarrays, Affymetrix™ oligonucleotide arrays and real-time quantitative PCR. It was shown to perform well, yielding transcript profiles that correlate well to the original, unamplified material, as well as being highly reproducible.</p><p>The developed method was applied to stem cell biology. The variability in gene expression between different populations of cultured neural stem cells (neurospheres) was investigated. It was shown that neurospheres isolated from different animals or passaged to different degrees show large fluctuations in gene expression, while neurospheres isolated and cultured under identical conditions are more similar and suitable for gene expression analysis. A second study showed that withdrawing epidermal growth factor (EGF) from the culture medium when treating the cells with an agent of interest has profound effects on gene expression, something which should be taken into consideration in future neurosphere studies.</p>