<p>In this study, the HCoV-NL63 genome was transcribed from RNA to DNA from which the M gene was amplified with various primers designed for use in specific expression systems. The various genes were cloned into the pGEM vector and confirmed by sequencing. The genes were now expressed in cloning vectors suited for each expression system (pFastBac for baculovirus expression, pFlexi for bacterial expression and pCMV for mammalian expression). Clones were sequenced for a second time. The recombinant clone in pFlexi was expressed in KRX cells and a 36hr time course was performed. The recombinant pFastBac clone was used to infect Sf9 insect cells and P1 and P2 viral stocks were obtained. The recombinant pCMV clone was used to transfect Cos1 mammalian cells.</p>

<p>In this study, the HCoV-NL63 genome was transcribed from RNA to DNA from which the M gene was amplified with various primers designed for use in specific expression systems. The various genes were cloned into the pGEM vector and confirmed by sequencing. The genes were now expressed in cloning vectors suited for each expression system (pFastBac for baculovirus expression, pFlexi for bacterial expression and pCMV for mammalian expression). Clones were sequenced for a second time. The recombinant clone in pFlexi was expressed in KRX cells and a 36hr time course was performed. The recombinant pFastBac clone was used to infect Sf9 insect cells and P1 and P2 viral stocks were obtained. The recombinant pCMV clone was used to transfect Cos1 mammalian cells.</p>

Magister Scientiae - MSc / Respiratory tract infections are one of the leading causes of morbidity and mortality across the world. This is especially true for infants, young children, the elderly and the immunocompromised. The strain placed on economies and health care systems of all countries by these diseases are phenomenal. Although we are familiar with various agents leading to these kinds of infections (e.g. rhino-, influenza-, parainfluenza, human metapneumo-, respiratory syncytial-, adeno- and coronaviruses), the cause of a substantial portion, (48-70%) of cases remain unidentified (Van der Hoek et al, 2004; Fouchier et al, 2004; File, 2003; Fine et al, 1999; Shay et al, 1999, Henrickson et al 2004; Murray et al 2001). In the past, human coronaviruses have not been known to cause severe disease in humans. For this reason, little research was performed on these viruses, with research focusing on the animal coronaviruses that are of veterinary importance. However, with the outbreak of SARS in 2003, the field of human coronavirus research has received significantly more attention. Also, the subsequent identification of two additional novel human coronaviruses (NL63 and HKU1) has led to an increased awareness of the potential threat of these viruses. With the discovery of these new human coronaviruses, it has become clear that the potential for another outbreak by a yet unknown human coronavirus is a very real possibility. This has made research into the pathogenesis and the role of the various coronavirus genes in the pathogenesis of these viruses of utmost importance. HCoV-NL63 was first discovered in January 2003 in the Netherlands. It causes upper and lower respiratory tract disease in young children, the elderly and immunocompromised individuals. The disease is also associated with croup and has even been implicated as a possible cause of the childhood vascular ailment Kawasaki Disease. HCoV-NL63 is frequently found in combination with other respiratory viruses leading to superinfections. It is still unclear whether HCoV-NL63 is an opportunistic virus or whether it leads the way for co-infection with other respiratory viruses. This particular virus is also the only coronavirus sharing the same cellular receptor as SARS-CoV. The virus is found all over the world and has been identified in countries like Australia (Arden et al, 2005), Japan (Ebihara et al., 2005; Suzuki et al., 2005), Belgium (Moës et al., 2005), Hong Kong (Chiu et al., 2005), Taiwan (Wu et al.,2007) Korea (Choi et al., 2006), Canada (Bastien et al., 2005), France (Vabret etal., 2005), Switzerland (Kaiser et al., 2005; Garbino et al., 2006), Germany (Vander Hoek et al., 2005), Sweden (Koetz et al., 2006) and South Africa (Smuts andHardie, 2006). In this study, the HCoV-NL63 genome was transcribed from RNA to DNA from which the M gene was amplified with various primers designed for use in specific expression systems. The various genes were cloned into the pGEM vector and confirmed by sequencing. The genes were now expressed in cloning vectors suited for each expression system (pFastBac for baculovirus expression, pFlexi for bacterial expression and pCMV for mammalian expression). Clones were sequenced for a second time. The recombinant clone in pFlexi was expressed in KRX cells and a 36hr time course was performed. The recombinant pFastBac clone was used to infect Sf9 insect cells and P1 and P2 viral stocks were obtained. The recombinant pCMV clone was used to transfect Cos1 mammalian cells. The genome was successfully transcribed and the M gene amplified and cloned into pGEM and confirmed by sequencing. Subsequent cloning of the various M genes into pFastBac for baculovirus expression, pFlexi for bacterial expression and pCMV for mammalian expression was achieved and sequencing confirmed the presence of the inserts in frame. pFlexi clones were successfully expressed in bacterial KRX cells with expression of the M protein in the pellet of the lysed bacterial cells. No M protein was seen in the supernatant of the lysed cells. Sf9 insect cells were infected with the recombinant pFastBac clones and P1 and P2 viral stocks were obtained. Protein expression occurs in KRX bacterial cells with optimal expression at approximately 24 hours. The M protein expresses on the cell membrane as can be concluded from the product obtained in the pellet of the lysed bacterial cells. Very little of the expressed protein is present in the plasma of the cell as evidenced by the absence of protein in the supernatant of the lysate. / South Africa

>Magister Scientiae - MSc / This is a pilot study to investigate the relationship between disease gene status and the structure of the human genome with specific reference to regions of recombination. It compares certain characteristics of a control set of genes, with no reported association or function in any known disease, with a second set of well-curated genes with a known association to a disease. One of the benefits of recombination is the introduction of new combinations of genetic variation in the genome. Recombination hotspots are regions on the chromosome where higher than normal frequencies of breaking and rejoining between homologous chromosomes occur during meiosis. The hotspot regions exhibit both a non-random distribution across the human genome and varying frequencies of breaking and rejoining. The study analyzed a set of features that represent general properties of human genes; namely base composition (percentage GC content), genetic variation (single nucleotide polymorphisms - SNPs), gene length, and positional effect (distance from chromosome end), in both the disease-associated gene set and the control set. These features were linked to recombination hotspots in the human genome and the frequency of recombination at these hotspots. Descriptive statistics was used to determine differences between the occurrences of these features in disease-associated genes compared to the control set, as well as differences in the occurrence of these same features in subset of genes containing an internal recombination hotspot compared to the genes with no internal recombination hotspot. The study found that disease-associated genes are generally longer than those in the control set, which is consistent with previous studies. It also found that disease-associated genes are much more likely to contain a recombination hotspot than those genes with no disease association. The study did not, however, find any association between disease gene status and the other set of features; namely GC content, SNP numbers or the position of a gene on the chromosome. Further analysis of the data suggested that the increased probability of disease-associated genes containing a recombination hotspot is most likely an effect of longer gene length and that the presence of a recombination hotspot is not sufficient in its own right to cause disease gene status.

Genome structure can be classified into three categories: primary structure, secondary structure and tertiary structure, and they are all important for gene transcription regulation. In this research, we utilize the structural information to characterize the correlations and interactions among genes, and involve such information into the Linear Mixed-Effects (LME) model to improve the accuracy of gene expression prediction. In particular, we use chromatin features as predictors and each gene is an observation. Before model training and testing, genes are grouped according to the genome structural information. We use four gene grouping methods: 1) grouping genes according to sliding windows on primary structure; 2) grouping anchor genes in chromatin loop structure; 3) grouping genes in the CTCF-anchored domain; and 4) grouping genes in the chromatin domains obtained from Hi-C experiments. We compare the prediction accuracy between LME model and linear regression model. If all chromatin feature predictors are included into the models, based on the primary structure only (Method 1), the LME models improve prediction accuracy by up to 1%. Based on the tertiary structure only (Methods 2-4), for the genes that can be grouped according the tertiary interaction data, LME models improve prediction accuracy by up to 2.1%. For individual chromatin feature predictors, the LME models improve from 2% to 26 %, in which improvement is more significant for chromatin features that have lower original predictive ability. For future research we propose a model that combines the primary and tertiary structure to infer the correlations among genes to further improve the prediction.

No description available.

Advances in next-generation sequencing (NGS) technologies have resulted in significant reduction of cost per sequenced base pair and increase in sequence data volume. On the other hand, most currently used NGS technologies produce relatively short sequence reads (50 - 150 bp) compared to Sanger sequencing (~700 bp). This represents an additional challenge in data analysis, because shorter reads are more difficult to assemble. At this point, production of sequencing data outpaces our capacity to analyze them. Newer NGS technologies capable of producing longer reads are emerging, which should simplify and speed up genome assembly. However, this will only increase the number of sequenced genomes without structural and functional annotation. In addition to multiple scientific initiatives to sequence thousands of genomes, personalized medicine centered on sequencing and analysis of individual human genomes will become more available. This poses a challenge for computer science and emphasizes the importance of developing new computational algorithms, methodology, tools, and pipelines. This dissertation focuses on development of these software tools, methodologies, and resources to help address the need for processing of volumes of data generated by new sequencing technologies. The research concentrated on genome structure analysis, individual variation, and comparative biology. This dissertation presents: (1) the Short Read Classification Pipeline (SRCP) for preliminary genome characterization of unsequenced genomes; (2) a novel methodology for phylogenetic analysis of closely related organisms or strains of the same organism without a sequenced genome; (3) a centralized online resource for standardized gene nomenclature. Utilizing the SRCP and the methodology for initial phylogenetic analysis developed in this dissertation enables positioning the organism in the evolutionary context. This should facilitate identification of orthologs between the species and paralogs within the species even in the initial stage of the analysis when only exome is sequenced and, thus, enable functional annotation by transferring gene nomenclature from well-annotated 1:1 orthologs, as required by the online standardized gene nomenclature resource developed in this dissertation. Thus, the tools, methodology, and resources presented here are tied together in following the initial analysis workflow for structural and functional annotation.

PIKE, L. M., HU, A., RENZAGLIA, K. S. and MUSICH, P. R., 1992. Liverwort genomes display extensive structural variations. Analyses of the total genomic DNA of eight species of liverworts and two species of green algae by thermal denaturation and CsCl buoyant density gradient centrifugation reveal a high degree of structural complexity and interspecific heterogeneity. The hepatic taxa exhibit two or more DNA components of varying base composition. Average G4‐C contents of total cellular DNA calculated from melting profiles are similarly variable, ranging from 38% to 53% G + C. The green alga Chara, a member of the ancestral line to land plants, shows similarities with liverworts in possessing multiple DNA components of comparable complexity, whereas Hydrodiciyon DNA displays a single component. Detailed hybridization analyses of individual density gradient fractions using α‐tubulin, rRNA and ribulose 1,5‐bisphosphate carboxylase/oxygenase large subunit rbcL gene probes were performed to locate the low‐copy number and moderately repetitive nuclear genes, and the chloroplast chromosome, respectively. The location of each gene within the density gradient is highly variable among the organisms examined; a‐tubulin occurs in fractions ranging from 44–64% G + C, rDNA in 50–64% G + C fractions, and the RbcL gene is located in fractions from 30–59% G + C. For a given species, the two nuclear genes normally overlap in their distributions within the gradient. In most instances, neither gene occurs in the major DNA components, indicating that these components may contain repetitive DNAs. The observed variation in the density of the rbcL gene implies substantial reorganization of the chloroplast genome. The overall differences in the genomic components within and between taxa provide insight into the dynamics of DNA structure that have occurred during the extended evolutionary history of these organisms.

Relaxin-like factor (RLF), also known as Leydig insulin-like peptide (Ley-I-L) or Insulin 3 (INSL3), is a newly characterized member of the insulin peptide family. Amino acid sequence homology revealed that RLF is more closely related to relaxin than any other insulin-like hormones. The main aim of this thesis was to sequence the rat RLF (Relaxin-like factor) gene and determine the structure and organisation of the gene. Secondly to compare the structural organisation of the rat RLF/JAK3 genomic region with that of the mouse and human, using bioinformatic databases. Thirdly to further investigate the signalling pathways for the RLF receptor, in particular the NFƒÛB pathway. The homology between rat and mouse in the JAK3/RLF region revealed 84.4 % similarity over 1262 bp of DNA sequence, observing that unlike the mouse, the rat RLF promoter is separated from the JAK3 gene by around 700-1000 bp. Similarly in humans, the RLF gene is located around 4 kb downstre am from JAK3. Also Protein kinase A (PKA) was the only signalling pathway which dispalyed major induction and no inhibitory effects were observed through the NFƒÛB signalling pathway.

De par sa taille (17 Gb), la complexité de son génome (allohexaploïde) ainsi que la forte proportion d’éléments répétés (>80%), l’étude du génome de blé tendre est une tâche particulièrement complexe et s’est souvent retrouvée confrontée aux limites technologies. Grâce une approche de tri de chromosomes, le chromosome 3B (995 Mb) a pu être isolé et séquencé. Ces données ont permis la construction d’une pseudomolécule. Mes travaux de thèse se sont basés sur des données de transcriptomique produites avec une approche RNA-Seq, afin d’investiguer l’impact de la taille de ce chromosome sur l’organisation de l’espace génique. L’annotation du chromosome 3B a permis de mettre en évidence : 5 326 gènes et 1 938 pseudogènes. L’analyse des librairies RNA-Seq pour 15 conditions de développement a permis de mettre en évidence l’expression de 71 % des gènes annotés, ainsi que 3 692 régions nouvellement transcrites (NTR). Nous avons aussi pu détecter des transcrits alternatifs pour 61% des gènes exprimés (en moyenne 6 isoformes). Nous avons donc pu mettre en évidence une structuration de l’espace génique pour le chromosome 3B. En effet, la transcription est répartie sur tout le chromosome, cependant les gènes sont organisés selon un gradient de densité croissant sur l’axe centromère-télomère. En nous basant sur le profil des données de recombinaison, nous avons divisé le chromosome en 3 régions : R1, R2 et R3. La région R2 correspondant à la région centrale du chromosome (647 Mb) où le taux de recombinaison est très faible voir absent. Les régions R1 (58 Mb) et R3 (69 Mb) correspondent respectivement aux parties distales du bras court et du bras long du chromosome, où le taux de recombinaison est le plus fort. Ces trois régions diffèrent par leur niveau et leur spécificité d'expression, ainsi que par leur structure génique (nombre d'exons, taille des introns …). En effet, les gènes ayant une expression tissu-spécifique, ainsi qu’un faible nombre de transcrits alternatifs sont retrouvés dans les régions R1 et R3. Deux modèles peuvent expliquer le lien observé entre la structure des gènes et leur niveau/spécificité d’expression : le modèle de la sélection pour l’économie et le modèle dessin génomique. En conclusion, ce travail a montré et ce, pour la première fois à l’échelle d’un chromosome entier de blé, l’impact de la taille du chromosome sur l’organisation ; mettant en relation la structure des gènes, leur niveau d’expression, leur spécificité d’expression, ainsi que leur nature évolutive. L’assemblage ainsi que l’annotation de pseudomolécules des autres chromosomes permettra de mettre en évidence si cette structure est conservée. Afin de mieux comprendre les mécanismes cellulaires impliqués dans la régulation de l’expression des gènes, une étude du paysage épigénomique a été engagée. / Genome-wide studies of the bread wheat are a complicated task due to its large size (17 Gb), its allohexaploidy and its high content in repeat sequences (>80%). Using a chromosome-specific approach, the chromosome 3B (995 Mb) was successfully isolated and sequenced leading to the assembly of one pseudomolecule. The work presented in this thesis investigated the impact of the 3B chromosome size on the gene space organization. Production of transcriptomic data was achieved using RNA-Seq approach. The chromosome 3B was annotated and we predicted 7 264 features, including 5 326 full genes and 1 938 pseudogenes. We constructed RNA-Seq libraries for 15 developmental wheat conditions. Using this data we detected expression of 71.4% of the predictions, and 3 692 novel transcribed regions (NTR). We also detected alternative transcripts for 61% of the expressed genes, with 5.8 isoforms on average for one gene. Using these transcriptional data, we highlighted a partitioning of the chromosome 3B gene space. Indeed, transcription was found all along the chromosome, but genes were organized according to an increasing density gradient along the centromere-telomere axis. Based on recombination profile, we segmented the chromosome in 3 major regions: R1, R2 and R3. The region R2 was identified with low or no recombination rate corresponding to the centromeric and peri-centromeric regions (647 Mb). The regions R1 and R3 were associated with a higher recombination rate, both localized on the distal part of the short arm (58 Mb) and the long arm (69 Mb) respectively, where the recombination rate is higher. All three regions showed distinct level and specificity of gene expression as well as unique gene structure (variation size, exon number, intron size). Indeed, genes expressed in a specific condition and with a small number of alternatives transcripts were localized on regions R1 and R3. We showed that two evolutionary model could explain the link between gene structure and the level/specificity of expression : “selection for economy” and “genome design”. In conclusion, a transcriptomic studies was achieved along the 3B chromosome for the first time. This study demonstrated a relationship between gene characteristics (structure, expression level, expression specificity and evolution) and the chromosome 3B organization. Future pseudomolecule assemblies will help us to assess the structural organization of these chromosomes. In order to better understand the cellular mechanisms of gene expression, an epigenomic study of the 3B chromosome was started.