Abstract This study was initiated to elucidate the complete genomic structures of type XV collagen in man and mouse and the functional properties of their promoters, as well as to obtain knowledge of the biological role of type XV collagen during development and maturity using immunofluorescence and transgenic techniques. The cloning and characterization of genomic clones revealed that the human COL15A1 gene is 145-kb in size and consists of 42 exons, and the mouse Col15a1 gene is 110-kb with 40 exons. The genomic organization of the two genes was found to be highly conserved, except for two regions of divergence. The nuclease S1 protection analysis revealed multiple transcription initiation sites in both genes, which is in accordance with the overall genomic structures of their 5'-flanking sequences. Transient cell transfection experiments with varying lengths of 5'-deletion constructs identified the fragments necessary for basic promoter activity in both genes and those implicated in the positive and negative regulation of the mouse Col15a1 gene. Furthermore, the involvement of transcription factor Sp1 in the gene regulation of the human COL15A1 gene was demonstrated. A mouse specific polyclonal antibody against type XV collagen was generated and utilized in the localization of type XV collagen protein in developing and mature mouse tissues. Type XV collagen was deposited early in the development and was particularly prominent in capillaries. Spatio-temporal differences in the expression of type XV collagen in various capillary types was demonstrated. Early expression was also detected in the skeletal muscle and peripheral nerves, while expression in the heart, lung, and kidney appeared to be developmentally regulated. Transgenic mice lines expressing truncated type XV collagen driven by either short or long endogenous type XV collagen promoters were generated. The two promoters conferred different tissue-specificities and expression levels, the longer one resulting in more endogenous-like expression. Despite some expression at both mRNA and protein levels, the truncated type XV collagen did not cause any obvious phenotypic or histological changes in any of the lines driven by the shorter promoter fragment. In heterozygote matings of one of the lines driven by the longer promoter fragment, however, a portion of the transgene positive mice appeared to be lost prenatally. Furthermore, pregnancy terminations in this line indicated a high number of abortions beginning at about 11 days of development. Further studies are needed before detailed conclusions on the consequences of the generated mutation can be drawn. The elucidation of the genomic structure of the human COL15A1 gene provides the necessary database for screening mutations in patient samples for candidate diseases caused by this collagen. The genomic clones and the mouse-specific antibody against type XV collagen are valuable tools also in future projects. The knowledge of the developmental dynamics of type XV collagen is of great value, as it helps to understand the physiological consequences that the as yet unidentified mutations in type XV collagen may cause in humans.

An automated detection process for Eukaryotic ribonucleic acid (RNA) Polymerase II Promoter is presented in this dissertation. We employ an artificial neural network (ANN) in conjunction with features that were selected using an information-theoretic approach. Firstly an introduction is given where the problem is described briefly. Some background is given about the biological and genetic principles involved in DNA, RNA and Promoter detection. The automation process is described with each step given in detail. This includes the data information gathering, feature generation, and the full ANN process. The ANN section of the project is split up in a generation process, a training section as well as a testing section. Lastly the final detection program was tested and compared to other promoter detection systems. An improvement of at least 10% in positive prediction value (PPV) in comparison with current state-of-the-art solutions was obtained. Note: A Companion CD should accompany this report that contains all the program code and some of the source data that was used in this project. All the references to “Companion CD”, reference number [18] are references to these programs.acquisition process, how the different samples were split into different sets and statistical. / Dissertation (MEng (Computer Engineering))--University of Pretoria, 2007. / Electrical, Electronic and Computer Engineering / unrestricted

Promoter region is the key regulatory region, which enables the gene to be transcribed or repressed by anchoring RNA polymerase and other transcription factors, but it is difficult to determine experimentally. Hence an in silico identification of promoters is crucial in order to guide experimental work and to pin point the key region that controls the transcription initiation of a gene. Analysis of various genome sequences in the vicinity of experimentally identified transcription start sites (TSSs) in prokaryotic as well as eukaryotic genomes had earlier indicated that they have several structural features in common, such as lower stability, higher curvature and less bendability, when compared with their neighboring regions. In this thesis work, the variation observed for these DNA sequence dependent structural properties have been used to identify and delineate promoter regions from other genomic regions. Since the number of bacterial genomes being sequenced is increasing very rapidly, it is crucial to have procedures for rapid and reliable annotation of their functional elements such as promoter regions, which control the expression of each gene or each transcription unit of the genome. The thesis work addresses this requirement and presents step by step protocols followed to get a generic method for promoter prediction that can be applicable across organisms. The each paragraph below gives an overall idea about the thesis organization into chapters. An overview of prokaryotic transcriptional regulation, structural polymorphism adapted by DNA molecule and its impact on transcriptional regulation has been discussed in introduction chapter of this thesis (chapter 1). Standardization of promoter prediction methodology - Part I Based on the difference in stability between neighboring upstream and downstream regions in the vicinity of experimentally determined transcription start sites, a promoter prediction algorithm has been developed to identify prokaryotic promoter sequences in whole genomes. The average free energy (E) over known promoter sequences and the difference (D) between E and the average free energy over the random sequence generated using the downstream region of known TSS (REav) are used to search for promoters in the genomic sequences. Using these cutoff values to predict promoter regions across entire E. coli genome, a reliability of 70% has been achieved, when the predicted promoters were cross verified against the 960 transcription start sites (TSSs) listed in the Ecocyc database. Reliable promoter prediction is obtained when these genome specific threshold values were used to search for promoters in the whole E. coli genome sequence. Annotation of the whole E. coli genome for promoter region has been carried out with 49% accuracy. Reference Rangannan, V. and Bansal, M. (2007) Identification and annotation of promoter regions inmicrobial genome sequences on the basis of DNA stability. J Biosci, 32, 851-862. Standardization of promoter prediction methodology - Part II In this chapter, it has been demonstrated that while the promoter regions are in general less stable than the flanking regions, their average free energy varies depending on the GC composition of the flanking genomic sequence. Therefore, a set of free energy threshold values (TSS based threshold values), from the genomic DNA with varying GC content in the vicinity of experimentally identified TSSs have been obtained. These threshold values have been used as generic criteria for predicting promoter regions in E. coli and B. subtilis and M. tuberculosis genomes, using an in-house developed tool ‘PromPredict’. On applying it to predict promoter regions corresponding to the 1144 and 612 experimentally validated TSSs in E. coli (genome %GC : 50.8) and B. subtilis (genome %GC : 43.5) sensitivity of 99% and 95% and precision values of 58% and 60%, respectively, were achieved. For the limited data set of 81 TSSs available for M. tuberculosis (65.6% GC) a sensitivity of 100% and precision of 49% was obtained. Reference Rangannan, V. and Bansal, M. (2009) Relative stability of DNA as a generic criterion for promoter prediction: whole genome annotation of microbial genomes with varying nucleotide base composition. Mol Biosyst, 5, 1758 - 1769. Standardization of promoter prediction methodology - Part III In this chapter, the promoter prediction algorithm and the threshold values have been improved to predict promoter regions on a large scale over 913 microbial genome sequences. The average free energy (AFE) values for the promoter regions as well as their downstream regions are found to differ, depending on their GC content even with respect to translation start sites (TLSs) from 913 microbial genomes. The TSS based cut-off values derived in chapter 3 do not have cut-off values for both extremes of GC-bins at 5% interval. Hence, threshold values have been derived from a subset of translation start sites (TLSs) from all microbial genomes which were categorized based on their GC-content. Interestingly the cut-off values derived with respect to TSS data set (chapter 3) and TLS data set are very similar for the in-between GC-bins. Therefore, TSS based cut-off values derived in chapter 2 with the TLS based cut-off values have been combined (denoted as TSS-TLS based cutoff values) to predict promoters over the complete genome sequences. An average recall value of 72% (which indicates the percentage of protein and RNA coding genes with predicted promoter regions assigned to them) and precision of 56% is achieved over the 913 microbial genome dataset. These predicted promoter regions have been given a reliability level (low, medium, high, very high and highest) based on the difference in its relative average free energy, which can help the users design their experiments with more confidence by using the predictions with higher reliability levels. Reference Rangannan, V. and Bansal, M. (2010) High Quality Annotation of Promoter Regions for 913 Bacterial Genomes. Bioinformatics, 26, 3043-3050. Web applications PromBase : The predicted promoter regions for 913 microbial genomes were deposited into a public domain database called, PromBase which can serve as a valuable resource for comparative genomics study for their general genomic features and also help the experimentalist to rapidly access the annotation of the promoter regions in any given genome. This database is freely accessible for the users via the World Wide Web http://nucleix.mbu.iisc.ernet.in/prombase/. EcoProm : EcoProm is a database that can identify and display the potential promoter regions corresponding to EcoCyc annotated TSS and genes. Also displays predictions for whole genomic sequence of E. coli and EcoProm is available at http://nucleix.mbu.iisc.ernet.in/ecoprom/index.htm. PromPredict : The generic promoter prediction methodology described in previous chapters has been implemented in to an algorithm ‘PromPredict’ and available at http://nucleix.mbu.iisc.ernet.in/prompredict/prompredict.html. Analysing the DNA structural characteristic of prokaryotic promoter sequences for their predominance Sequence dependent structural properties and their variation in genomic DNA are important in controlling several crucial processes such as transcription, replication, recombination and chromatin compaction. In this chapter 6, quantitative analysis of sequences motifs as well as sequence dependent structural properties, such as curvature, bendability and stability in the upstream region of TSS and TLS from E. coli, B. subtilis and M. tuberculosis has been carried out in order to assess their predictive power for promoter regions. Also the correlation between these structural properties and GC-content has been investigated. Our results have shown that AFE values (stability) gives finer discrimination rather than %GC in identifying promoter regions and stability have shown to be the better structural property in delineating promoter regions from non-promoter regions. Analysis of these DNA structural properties has been carried out in human promoter sequences and observed to be correlating with the inactivation status of the X-linked genes in human genome. Since, it is deviating from the theme of main thesis; this chapter has been included as appendix A to the main thesis. General conclusion Stability is the ubiquitous DNA structural property seen in promoter regions. Stability shows finer discrimination for promoter prediction rather than directly using %GC-content. Based on relative stability of DNA, a generic promoter prediction algorithm has been developed and implemented to predict promoter regions on a large scale over 913 microbial genome sequences. The analysis of the predicted regions across organisms showed highly reliable predictive performance of the algorithm.

This thesis deals with measurement of customer satisfaction and loyalty among health care providers with services provided by particular pharmaceutical company. The main goal of this work is to develop methodology for measuring customer satisfaction and loyalty for this segment of services. The theoretical part deals with a summary of findings and approaches describing satisfaction and loyalty, and also review of the methods used for their determination. The thesis also analyzes the differences between two groups of customers and summarizes the differences identified in the primary research that was conducted. Primary research was conducted by having structured interviews with respondents from among health care providers (private versus state providers). Dissertation's conclusions correspond with professional resources and thus develop areas of knowledge about the characteristics of the examined service segment.

Maintaining cells in an undifferentiated totipotent state is essential for initiating developmental programs that lead to a fully formed organism in each generation and for maintaining immortal germ cells across generations. Caenorhabditis elegans is a powerful genetic model organism to study early germ cell development due to the animal’s transparency and the ability to screen for mutant phenotypes. However, our ability to use standard techniques to study gene expression using fluorescent reporter genes has been limited due to germline-specific silencing mechanisms that repress transgenes. Therefore, we lack even basic knowledge of how expression is regulated in C. elegans germ cells. In this study, we develop methods to overcome these silencing mechanisms by using a class of noncoding DNA, called Periodic An/Tn Clusters (PATCs), to prevent transgene silencing in the germline. We use these improved tools to test the proposed role of putative germline-specific regulatory DNA motifs and the role a periodic TT signal within germline promoters. We fused GFP to the promoter of a germline expressed gene (pcn-1), which is enriched for PATCs and contains a germline-specific motif (TTAAAG). Our results show that despite enrichment and phylogenetic conservation, the TTAAAG motif is not required for germline expression. To test additional motifs and periodic TTs, we have designed a system that will allow us to test synthetic gene fragments for bi-directional germline expression. These tools will allow us to rapidly test motif redundancy, motif spacing, and TT periodicity using gfp and rfp signals in the germline and will enable experiments aimed at understanding the role of germline regulatory elements.

京都大学 / 0048 / 新制・課程博士 / 博士(農学) / 甲第22707号 / 農博第2423号 / 新制||農||1080(附属図書館) / 学位論文||R2||N5300(農学部図書室) / 京都大学大学院農学研究科地域環境科学専攻 / (主査)教授 本田 与一, 教授 田中 千尋, 教授 吉村 剛 / 学位規則第4条第1項該当 / Doctor of Agricultural Science / Kyoto University / DGAM

Understanding DNA sequences has been an ongoing endeavour within bioinfor- matics research. Recognizing the functionality of DNA sequences is a non-trivial and complex task that can bring insights into understanding DNA. In this thesis, we study deep learning models for recognizing gene regulating regions of DNA, more specifi- cally promoters. We first consider DNA modelling as a language by training natural language processing models to recognize promoters. Afterwards, we delve into current models from the literature to learn how they achieve their results. Previous works have focused on limited curated datasets to both train and evaluate their models using cross-validation, obtaining high-performing results across a variety of metrics. We implement and compare three models from the literature against each other, us- ing their datasets interchangeably throughout the comparison tests. This highlights shortcomings within the training and testing datasets for these models, prompting us to create a robust promoter recognition testing dataset and developing a testing methodology, that creates a wide variety of testing datasets for promoter recognition. We then, test the models from the literature with the newly created datasets and highlight considerations to take in choosing a training dataset. To help others avoid such issues in the future, we open-source our findings and testing methodology. / Graduate

The bacteroides group is a subdivision in the <I>Cytophaga-Flavobacterium-Bacteroides</I> phylum. This group is as phylogenetically distinct from other Gram-negative enterics, including <I>Escherichia coli</I>, as they are from Gram-positive organisms. Furthermore, there is no cross expression between genes of <I>E. coli</I> and <I>Bacteroides</I> species. It is thought that this difference in gene expression lies in part at the level of transcription initiation and is due to the sequences within the promoter region itself. A putative consensus sequence for <I>Bacteroides</I> promoters has been published by C. Jeff Smith&#146;s research group based on alignments of the sequences upstream of certain regulated genes. However, this consensus has not been found within all putative <I>Bacteroides</I> promoters. In this study, the promoter structure and function of a strong housekeeping <I>B. thetaiotaomicron</I> 16S rRNA promoter was examined and compared to an <I>E. coli</I> 16S rRNA promoter. Our hypothesis is that there are significant differences between the promoters of these two organisms. Analysis of <I>B. thetaiotaomicron</I> sequence upstream of the 16S rRNA gene has revealed the same overall structure known for <I>E. coli</I> 16S rRNA promoters in that there are two putative promoters separated by approximately 150 bp. However, the <I>B. thetaiotaomicron</I> 16S rRNA promoter contains the proposed <I>Bacteroides</I> &#151;7 and &#151;33 consensus sequences instead of the well known <I>E. coli</I> &#151;10 and &#151;35 consensus sequences. The biological activity of the<I> B. thetaiotaomicron</I> 16S rRNA full-length promoter was confirmed using a <I>Bacteroides lux</I> reporter system. A newly designed <I>Bacteroides lux</I> reporter was used to analyze specific regions of the <I>B. thetaiotaomicron</I> 16S rRNA promoter. In addition, by pairing the <I>B. thetaiotaomicron</I> 16S rRNA promoter with an <I>E. coli</I> ribosomal binding site, and vice-versa, the improved <I>lux</I> reporter was used to further confirm that the difference in gene expression between the two species lies at the level of transcription in <I>E. coli</I>. In <I>Bacteroides</I>, however, transcription and translation may work together to create a barrier to efficient gene expression of foreign genes. </P> / Master of Science

We previously reported that the CNGB1 locus encoding the rod photoreceptor cGMP-gated channel β-subunit is complex, comprising non- overlapping transcription units that give rise to at least six transcripts (Ardell, M.D., Aragon, I., Oliveira, L., Porche, G.E., Burke, E., Pittler, S.J., 1996. The beta subunit of human rod photoreceptor cGMP-gated cation channel is generated from a complex transcription unit. FEBS Lett. 389, 213- 218). To further understand the transcriptional regulation of this extraordinarily complex locus, and to develop a screen for defects in the gene in patients with hereditary disease, we determined its genomic organization and DNA sequence. The CNGB1 locus consists of 33 exons, which span approximately 100 kb of genomic DNA on chromosome 16. The β-subunit comprises two domains, an N-terminal glutamic acid-rich segment (GARP), and a C-terminal channel-like portion. Two additional exons encoding a short GARP transcript and a truncated channel-like transcript have been identified. A major transcription start point was identified 79 bp upstream of the initiator ATG. To begin analysis of the basis for the generation of multiple transcripts, and to identify promoters driving expression in retina, approximately 2.5 kb of the upstream region were sequenced. Putative cis- elements, which can bind the retina-specific transcription factors Crx and Erx, were found immediately upstream of the transcription start point, and may be important for gene expression in this tissue. From our analysis, a model is reported to account for at least four of the retinal transcripts.

Objectives: Breast cancer resistance protein (BCRP) confers resistance to certain anticancer drugs such as mitoxantrone, topotecan and SN-38. A putative estrogen response element (ERE) was located in the promoter region of the BCRP gene. The present study aimed to investigate whether human BCRP expression is regulated pretranscriptionally by 17β-estradiol. Methods: Two recombinant plasmids (pcDNA3-promoter-BCRP and pcDNA3-CMV-BCRP) were designed to express the full-length BCRP cDNA enforced driven by its endogenous promoter containing a functional ERE and a control constitutive cytomegalovirus (CMV) promoter, respectively, which were transfected into estrogen receptor α (ERα)-positive MCF-7 and ERα-negative MDA-MB-231 breast cancer cell lines. Results: 17β-Estradiol significantly upregulated BCRP mRNA and protein expression in a dose-dependent manner, and the effect was abolished by the antiestrogen tamoxifen. Furthermore, electrophoretic mobility shift assays demonstrated that the putative ERE in the promoter region of the BCRP gene and ERα are essential for transcriptional activation of BCRP by 17β-estradiol. Conclusions: Taken together, our findings indicate that BCRP expression is upregulated by 17β-estradiol via a novel pretranscriptional mechanism which might be involved in 17β-estradiol-ER complexes binding to the ERE of BCRP promoter via the classical pathway to activate transcription of the BCRP gene.