Recently, it has been shown that genetic variation that perturbs the regulation of gene expression is widespread in eukaryotic genomes. Regulatory variation (RV) is expected to be an important driver of phenotypic differences, evolutionary change, and susceptibility to complex genetic diseases. Because trans-acting regulators of gene expression control mRNA levels of multiple genes simultaneously, we hypothesise that RV that affects these components will have a shared-influence upon the expression levels of multiple genes. Since genes are regulated in trans by combinations of basal and tissue specific factors, we further hypothesise that RV in these components may have different effects in each tissue. We used microarrays to identify 755 genes that were affected by RV in at least one of the brain, kidney and liver of two inbred mouse strains, C57BL/6J and DBA/2J. Just 2% were affected in all three tissues, suggesting that the influence of RV is predominantly tissue specific. To study shared-RV, we measured the expression levels of these 755 genes in the same 3 tissues from a panel of recombinant inbred mice, and identified groups of correlated genes that are putatively under the influence of shared trans-acting RV. Using methods that we developed for studying the effects of RV in multiple tissues, we identified 212 genes that are correlated in all three tissues, which include 10 groups of at least 3 genes. We developed a novel method called coherency analysis to show that RV consistently affected the expression levels of these groups of genes in different genetic backgrounds. Strikingly, the relative up- or down-regulation of genes in each group was markedly different in the three tissues of the same mouse, suggesting that the influence of RV itself is not tissue specific as previously expected, but that RV can influence genes with differing outcomes in each tissue. These observations are compatible with RV affecting combinations of basal and tissue specific regulatory factors. This is the first cross-tissue investigation into the influence of shared-RV in multiple tissues, which has important implications in humans, where access to the phenotypically relevant tissue may be necessarily limited.

Recently, it has been shown that genetic variation that perturbs the regulation of gene expression is widespread in eukaryotic genomes. Regulatory variation (RV) is expected to be an important driver of phenotypic differences, evolutionary change, and susceptibility to complex genetic diseases. Because trans-acting regulators of gene expression control mRNA levels of multiple genes simultaneously, we hypothesise that RV that affects these components will have a shared-influence upon the expression levels of multiple genes. Since genes are regulated in trans by combinations of basal and tissue specific factors, we further hypothesise that RV in these components may have different effects in each tissue. We used microarrays to identify 755 genes that were affected by RV in at least one of the brain, kidney and liver of two inbred mouse strains, C57BL/6J and DBA/2J. Just 2% were affected in all three tissues, suggesting that the influence of RV is predominantly tissue specific. To study shared-RV, we measured the expression levels of these 755 genes in the same 3 tissues from a panel of recombinant inbred mice, and identified groups of correlated genes that are putatively under the influence of shared trans-acting RV. Using methods that we developed for studying the effects of RV in multiple tissues, we identified 212 genes that are correlated in all three tissues, which include 10 groups of at least 3 genes. We developed a novel method called coherency analysis to show that RV consistently affected the expression levels of these groups of genes in different genetic backgrounds. Strikingly, the relative up- or down-regulation of genes in each group was markedly different in the three tissues of the same mouse, suggesting that the influence of RV itself is not tissue specific as previously expected, but that RV can influence genes with differing outcomes in each tissue. These observations are compatible with RV affecting combinations of basal and tissue specific regulatory factors. This is the first cross-tissue investigation into the influence of shared-RV in multiple tissues, which has important implications in humans, where access to the phenotypically relevant tissue may be necessarily limited.

Recently, it has been shown that genetic variation that perturbs the regulation of gene expression is widespread in eukaryotic genomes. Regulatory variation (RV) is expected to be an important driver of phenotypic differences, evolutionary change, and susceptibility to complex genetic diseases. Because trans-acting regulators of gene expression control mRNA levels of multiple genes simultaneously, we hypothesise that RV that affects these components will have a shared-influence upon the expression levels of multiple genes. Since genes are regulated in trans by combinations of basal and tissue specific factors, we further hypothesise that RV in these components may have different effects in each tissue. We used microarrays to identify 755 genes that were affected by RV in at least one of the brain, kidney and liver of two inbred mouse strains, C57BL/6J and DBA/2J. Just 2% were affected in all three tissues, suggesting that the influence of RV is predominantly tissue specific. To study shared-RV, we measured the expression levels of these 755 genes in the same 3 tissues from a panel of recombinant inbred mice, and identified groups of correlated genes that are putatively under the influence of shared trans-acting RV. Using methods that we developed for studying the effects of RV in multiple tissues, we identified 212 genes that are correlated in all three tissues, which include 10 groups of at least 3 genes. We developed a novel method called coherency analysis to show that RV consistently affected the expression levels of these groups of genes in different genetic backgrounds. Strikingly, the relative up- or down-regulation of genes in each group was markedly different in the three tissues of the same mouse, suggesting that the influence of RV itself is not tissue specific as previously expected, but that RV can influence genes with differing outcomes in each tissue. These observations are compatible with RV affecting combinations of basal and tissue specific regulatory factors. This is the first cross-tissue investigation into the influence of shared-RV in multiple tissues, which has important implications in humans, where access to the phenotypically relevant tissue may be necessarily limited.

Nucleic acid and protein databases such as GenBank are growing at a rate that perhaps eclipses even Moore’s Law of increase in computational power. This poses a problem for the biological sciences, which have become increasingly dependant on searching and manipulating these databases. It was once reasonably practical to perform exhaustive searches of these databases, for example using the algorithm described by Smith and Waterman, however it has been many years since this was the case. This has led to the development of a series of search algorithms, such as FASTA, BLAST and BLAT, that are each successively faster, but at similarly successive costs in terms of thoroughness. Attempts have been made to remedy this problem by devising search algorithms that are both fast and thorough. An example is CAFE, which seeks to construct a search system with a sub-linear relationship between search time and database size, and argues that this property must be present for any search system to be successful in the long term. This dissertation explores this notion by seeking to construct a search system that takes advantage of the growing redundancy in databases such as GenBank in order to reduce both the search time and the space required to store the databases and their indices, while preserving or increasing the thoroughness of the search. The result is the creation and implementation of new genomic sequence search and alignment, database compression, and index compression algorithms and systems that make progress toward resolving the problem of reducing search speed and space requirements while improving sensitivity. However, success is tempered by the need for databases with adequate local redundancy, and the computational cost of these algorithms when servicing un-batched queries.

Gene expression and its regulation is a highly coordinated system, involved in many biological processes such as cell growth, division and differentiation. Transcriptional regions, involved in gene regulation, consist of a heterogeneous collection of smaller regulatory elements. In some cases, co-regulated genes contain a common set of transcription factor binding sites (TFBS). Analysis of promoter regions is the major approach in understanding the transcriptional regulatory mechanisms. It is also useful for interpretation of mammalian gene expression studies, where co-expressed genes may share motifs representing putative TFBS. Motif identification also has the advantage that it can predict control regions in genes that have not been measured experimentally. However, a common problem is incomplete genomic sequence for the experimental species of interest. The approach here is to identify and use orthologous gene promoter sequences from a related and well-characterised species. The primary aim of this study was to identify and predict regulatory TFBS in species where promoter sequence does not exist or is incomplete. The MEME programme was employed for the motif prediction step. The predicted elements were subsequently compared to known TFBS using TRANSFAC and JASPAR databases for identification. A methodology based on relative entropy was used. The validity of the method was confirmed as the predicted motifs in the training set were the expected sites involved in regulation of muscle development. The technique was applied to two data sets, generated from expressed sequence tag (EST) clustering analysis and microarray experiments. All data sets, software and results are available on the accompanying CD. Bovine expression data was analysed for cardiac-specific expression using two separate approaches, combining bovine library EST frequency and human gene expression ratios. For each approach, the orthologous human and bovine promoter sequences were analysed for common motifs. Across all comparisons, 37% of motifs were identified as known TFBS using the TRANSFAC and JASPAR databases. As the human comparison had more promoter sequences available, this was the main limiting factor for the corresponding bovine analysis, rather than cross-species divergence or accuracy of gene expression measurement. Results from this study demonstrate that using promoter sequences from a related species is a viable approach when studying gene expression in species with limited amount of genomic sequence. As the bovine genome becomes more complete, it can in turn serve as the reference genome for other agriculturally important ruminants, such as sheep, goat and deer. The second application concerned in silico analysis of gene regulation patterns in response to stimuli. Recently it has been shown that a mutation in the bone morphogenetic receptor IB leads to an increased ovulation rate in sheep. The objective of this study was to analyse gene expression patterns in cultured cells in response to four members of the BMP family, i.e. BMP2, BMP4, BMP6 and BMP7 and the control TGFβ. Microarray data was provided by J. Young. Twelve highly upregulated genes were stimulated by all BMPs, seven of which are known BMP target genes. Analysis of the predicted motifs identified four elements that may be involved in the regulation process. Cross-species comparison for one of the genes, ID1, showed high conservation of one of the motifs across 11 mammalian genomes. This particular motif had not been identified as a known binding site. In summary, the analysis of the expression data suggest an extension of the list of BMP targets. The proposed method is relatively robust when sufficiently co-expressed (co-regulated) sequences can be identified, whether from the same or another species.

<p>Proteins play a crucial roll in all biological processes. The wide range of protein functions is made possible through the many different conformations that the protein chain can adopt. The structure of a protein is extremely important for its function, but to determine the structure of protein experimentally is both difficult and time consuming. In fact with the current methods it is not possible to study all the billions of proteins in the world by experiments. Hence, for the vast majority of proteins the only way to get structural information is through the use of a method that predicts the structure of a protein based on the amino acid sequence.</p><p>This thesis focuses on improving the current protein structure prediction methods by combining different prediction approaches together with machine-learning techniques. This work has resulted in some of the best automatic servers in world – Pcons and Pmodeller. As a part of the improvement of our automatic servers, I have also developed one of the best methods for predicting the quality of a protein model – ProQ. In addition, I have also developed methods to predict the local quality of a protein, based on the structure – ProQres and based on evolutionary information – ProQprof. Finally, I have also performed the first large-scale benchmark of publicly available homology modeling programs.</p>

<p>A tremendous amount of genomic sequence data of relatively high quality has become publicly available due to the human genome sequencing projects that were completed a few years ago. Despite considerable efforts, we do not yet know everything that is to know about the various parts of the genome, what all the regions code for, and how their gene products contribute in the myriad of biological processes that are performed within the cells. New high-performance methods are needed to extract knowledge from this vast amount of information.</p><p>Furthermore, the traditional view that DNA codes for RNA that codes for protein, which is known as the central dogma of molecular biology, seems to be only part of the story. The discovery of many non-proteincoding gene families with housekeeping and regulatory functions brings an entirely new perspective to molecular biology. Also, sequence analysis of the new gene families require new methods, as there are significant differences between protein-coding and non-protein-coding genes.</p><p>This work describes a new search processor that can search for complex patterns in sequence data for which no efficient lookup-index is known. When several chips are mounted on search cards that are fitted into PCs in a small cluster configuration, the system’s performance is orders of magnitude higher than that of comparable solutions for selected applications. The applications treated in this work fall into two main categories, namely pattern screening and data mining, and both take advantage of the search capacity of the cluster to achieve adequate performance. Specifically, the thesis describes an interactive system for exploration of all types of genomic sequence data. Moreover, a genetic programming-based data mining system finds classifiers that consist of potentially complex patterns that are characteristic for groups of sequences. The screening and mining capacity has been used to develop an algorithm for identification of new non-protein-coding genes in bacteria; a system for rational design of effective and specific short interfering RNA for sequence-specific silencing of protein-coding genes; and an improved algorithmic step for identification of new regulatory targets for the microRNA family of non-protein-coding genes.</p> / Paper V, VI, and VII are reprinted with kind permision of Elsevier, sciencedirect.com

Thesis (M. Phil.)--University of Hong Kong, 2006. / Title proper from title frame. Also available in printed format.

Due to the duration of human ageing, researchers must rely on models such as animals and cells. Replicative senescence and stress-induced premature senescence (SIPS) are two cellular models sharing many features. Although telomeres play a major role in replicative senescence, their involvement in SIPS is unclear. In this work, we first wanted to investigate how accurate models of ageing are. We published a new model of the evolution of human ageing, which offers a refined view of the evolution of ageing in humans and suggests that human models should be favoured. Though studying other mammals, reptiles, and birds may also be useful, we conclude that lower life forms such as yeast and invertebrates are not representative of the human ageing process. Secondly, we wanted to elucidate the importance of telomeres in SIPS and study gene expression and regulatory networks. Using a telomerase-immortalized cell line, we found no evidence that damage specific to the telomeres is at the origin of SIPS. In our published model, neither the TGF-â1 pathway nor telomeres appear to play a crucial role in SIPS. We suggest that widespread damage to the DNA causes SIPS and propose a rearrangement of gene expression networks as a result of stress. Moreover, we advise caution in using telomerase in anti-ageing therapies since telomerase expression may alter the normal cellular functions and promote tumorogenesis. Lastly, we published strategies to integrate the modern computational approaches to research ageing. Although we find it unlikely that a full understanding of ageing may be achieved within a near future, we argue that understanding the structure and finding key regulatory genes of the human ageing process is possible.

Hox transcription factors are extensively investigated in diverse fields of molecular and evolutionary biology. Hox genes belong to the family of homeobox transcription factors characterised by a 60 amino acids region called homeodomain. These genes are evolutionary conserved and play crucial roles in the development of animals. In particular, they are involved in the specification of segmental identity, and in the tetrapod limb differentiation. In vertebrates, this family of genes can be divided into 14 groups of homology. Common methods to classify Hox proteins focus on the homeodomain. Classification is however hampered by the high conservation of this short domain. Since phylogenetic tree reconstruction is time-consuming, it is not suitable to classify the growing number of Hox sequences. The first goal of this thesis is therefore to design an automated approach to classify vertebrate Hox proteins in their groups of homology. This approach classifies Hox proteins on the basis of their scores for a combination of protein generalised profiles. The resulting program, HoxPred, combines predictive accuracy and time efficiency. We used this program to detect and classify Hox genes in several teleost fish genomes. In particular, it allowed us to clarify the evolutionary history of the HoxC1a genes in teleosts. Overall, HoxPred could efficiently contribute to the bioinformatics toolbox commonly used to annotate vertebrate Hox sequences. This program was then evaluated in non-vertebrate species. Although not intended for the classification of Hox proteins in distantly related species, HoxPred showed a high accuracy in bilaterians. It has also given insights into the evolutionary relationships between bilaterian posterior Hox genes, which are notoriously difficult to classify with phylogenetic trees. As transcription factors, Hox proteins regulate target genes by specifically binding DNA on cis-regulatory elements. Only a few of these target genes have been identified so far. The second goal of this work was to evaluate whether it is possible to apply computational approaches to detect Hox cis-regulatory elements in genomic sequences. Regulatory Sequence Analysis Tools (RSAT) is a suite of bioinformatics tools dedicated to the detection of cis-regulatory elements in genomes. We participated to the development of matrix-based pattern matching approaches in RSAT. After having performed a statistical validation of the pattern-matching scores, we focused on a study case based on the vertebrate HoxB1 protein, which binds DNA with its cofactors Pbx and Meis. This study aimed at predicting combinations of cis-regulatory elements for these three transcription factors.