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The objective of this project was to explore the role of nutrients, mainly glucose and fatty acids, in the regulation of insulin gene expression via the complex interaction factors and positive or negative <i>cis</i>-acting DNA elements located throughout the promoter region, which is positioned up to 300bp. Insulin is a vital endocrine hormone, for the regulation of growth and metabolism. Synthesis of insulin occurs in pancreatic cells. The regulation of tissue- and temporal-specific insulin gene expression and its activation in response to extracellular inducers are two fundamental processes that attract many molecular biologists. Therefore to achieve the objective three major approaches were undertaken in this project. 1. Characterizing and identifying the transcription factors that bound to the negative regulatory element (NRE) within the region -258 to -280 of the human insulin promoter. This was done by applying a promising and unique method including a proteomic approach. The major findings propose NRE bind multiple positive and negative factors. 2. Studying in detail the effect of glucose on the factors that bound to the - 230 to -280 region which included NRE binding factors and PDX-1 which binds A-boxes, in various pancreatic rodent cell lines and isolated human islet of Langerhans. This study shows that the glucose stimulating response of the NRE in b cell lines and human islets does not require synergy between adjacent sequence elements such as E2 binding USF and A5 binding the PDX-1. Therefore, the NRE binding factors are able to demonstrate convincingly the existence of short-term transcriptional control of the insulin gene regulated by glucose. 3. Studying the expression of the lipogenic transcription factor SREBPs in the islets of Langerhans and their role in controlling the expression of several genes. The present study provided the first demonstration of the role of SREBPs in mediating the effect of nutrient signals, particularly glucose, on the insulin promoter and its mRNA.

The translation of a series of <I>cat </I>mRNAs containing either the <I>HSP26 </I>5'- leader or various artificial 5'-leaders (Vega Laso <I>et al., </I>1993) were analysed during growth. From this study, the relative translational efficiencies of these mRNAs were shown to vary from 2% to 100% during mid-exponential phase as observed previously (Vega Laso <I>et al.,</I>1993). However, upon analysing the translation of the various <I>cat </I>constructs during growth, their relative translational efficiencies did not change significantly as yeast cells approached stationary phase. A new set of <I>lacZ </I>mRNAs carrying different natural 5'-leaders (<I>PGK1, PYK1, RpL3, Rp29, GDH1, HSP26, HSP12 </I>and <I>TH14</I>) were constructed. These <I>lacZ </I>mRNAs were placed under the control of the promoters taken from genes expressed during different phases of growth (<I>PGK1 </I>and <I>HSP26</I>). Even though the various <I>PGK1-lacZ </I>and <I>HSP26-lacZ </I>mRNAs were translated differentially, the ability of these mRNAs to compete for the translational apparatus did not appear to change as cells entered stationary phase. The translation of a variety of natural mRNAs encoding a wide range of functions was then analysed by determining their polysomal distribution at various points during growth. Irrespective of the growth phase, a large proportion of each mRNA was detected in the polysomal fractions, suggesting that they continued to be translated in stationary phase. Overall, the data strongly suggest that, under the conditions tested, an excess translational capacity exists in stationary phase yeast cells. Hence gene expression may be largely regulated by transcription upon entry to stationary phase.

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The communication of facial affect is a poorly understood process. In a subthreshold priming task, subjects were asked to rate photographs of faces displayed in a tachistoscope. Faces exhibiting strong positive and negative expressions were shown at 10% below the subject's recognition level and masked. Following this, a photograph of the same individual exhibiting no expression, or neutrality, was exposed at a rate that was well above the subject's recognition level. The subject was asked to rate the second photograph, or target, as being either positive or negative. It was hypothesized that the evaluations of target photographs would be biased by the prior subthreshold presentation of a strong positive or negative prime. The results did not support the hypothesis. Subjects rated the neutral faces as being negative regardless of the prime. It is possible that the experimental procedure produced a negative bias that counteracted the potential biasing effect of the primes.

Differently expressed genes e.g. in a disease may play a role in the etiology or progression of the disease. The traditional approach of finding differentially expressed genes is to compare the expression levels in the groups, and produce a list of differentially expressed candidate genes. With many pairwise comparisons, the risk of introducing type I and type II errors is high. One solution is to group together genes that are co-expressed into modules. Weighted gene coexpression network analysis (WGCNA) uses a topological overlap module approach and has been proved to find patterns that have been undetected by gene-to-gene comparison methods. qPCR has high sensitivity and specificity, and advances in technology has increased its throughput. The goal of the project was to construct WGCNA modules from qPCR data and evaluate the WGCNA method in five previously published qPCR data sets. There was little overlap between the differentially expressed genes found in the published articles and the candidates found by WGCNA. In three data sets WGCNA failed to produce any significant genes. In one of the data set significant genes were found where the original article failed. In one data set, 19 out of 60 genes that are top-ranked by the original authors were found in significant WGCNA modules. The biggest challenge with this type of comparison is to determine whether results that differ from the published studies are more or less biologically relevant. It is difficult to draw conclusions on whether the method is suitable for use for analysis of qPCR data based on this study.

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No description available.