No / Incorporating edge activators (surfactants) into liposomes was shown previously to improve estradiol vesicular skin delivery; this phenomenon was concentration dependent with low or high concentrations being less effective. Replacing surfactants with limonene produced similar behaviour, but oleic acid effects were linear with concentration up to 16% (w/w), beyond which it was incompatible with the phospholipid. This present study thus employed high sensitivity differential scanning calorimetry to probe interactions of additives with dipalmitoylphosphatidylcholine (DPPC) membranes to explain such results. Cholesterol was included as an example of a membrane stabiliser that removed the DPPC pre-transition and produced vesicles with a higher transition temperature (Tm). Surfactants also removed the lipid pre-transition but reduced Tm and co-operativity of the main peak. At higher concentrations, surfactants also formed new species, possibly mixed micelles with a lower Tm. The formation of mixed micelles may explain reduced skin delivery from liposomes containing high concentrations of surfactants. Limonene did not remove the pre-transition but reduced Tm and co-operativity of the main peak, apparently forming new species at high concentrations, again correlating with vesicular delivery of estradiol. Oleic acid obliterated the pre-transition. The Tm and the co-operativity of the main peak were reduced with oleic acid concentrations up to 33.2 mol%, above which there was no further change. At higher concentrations, phase separation was evident, confirming previous skin transport findings.

No / Piperacillin, a potent ß-lactam antibiotic, is effective in a large variety of Gram+ and Gram¿ bacterial infections but its administration is limited to the parenteral route as it is not absorbed when given orally. In an attempt to overcome this problem, we have synthesized a novel series of charged liposaccharide complexes of piperacillin comprising a sugar moiety derived from d-glucose conjugated to a lipoamino acid residue with varying side-chain length (cationic entity) and the piperacillin anion. A complete multiple reaction monitoring LC¿MS/MS method was developed to detect and characterize the synthesized complexes. The same method was then successfully applied to assess the in vitro apparent permeability values of the charged liposaccharide complexes in Caco-2 monolayers. / BBSRC

No description available.

Der Transkriptionsfaktor CCAAT/Enhancer Binding Protein beta (C/EBPbeta) reguliert die Genexpression, Proliferation und Differenzierung in verschiedenen Zelltypen. Die Funktion von C/EBPbeta wird durch Interaktionen mit einer Reihe von Kofaktoren moduliert, die Bestandteile von Chromatin-verŠndernden oder Transkriptions-regulierenden makromolekularen Maschinen sind. Die Identifikation und funktionelle Charakterisierung dieser Kofaktoren trŠgt ma§geblich zum VerstŠndnis der Biologie von C/EBPbeta bei. C/EBPbeta wird zudem in vielfŠltiger Weise posttranslational reguliert. Beispielsweise kann C/EBPbeta phosphoryliert, SUMOyliert, acetyliert und an mehreren Positionen an Arginin- und Lysinresten methyliert werden. Die SUMOylierung von C/EBPbeta gilt als SchlŸsselmodifikation, die nachfolgende Modifikationen steuert und zu einer VerŠnderung der genregulatorischen Eigenschaften von C/EBPbeta fŸhrt. C/EBPbeta bindet an zwei Enzyme der SUMOylierungsmaschinerie, Ubc9 und PIAS3. Es konnte gezeigt werden, dass PIAS3 nicht nur als E3-Ligase bei der SUMOylierungsreaktion dient, sondern auch mit SUMO-modifiziertem C/EBPbeta interagieren und als transkriptioneller Repressor wirken kann. Um weitere Interaktionspartner von C/EBPbeta zu identifizieren wurde ein System zur Proteom-weiten Identifikation von Bindungspartnern etabliert. Dazu wurden radioaktiv markierbare Proteinsonden hergestellt, welche die Identifikation von Bindungspartnern auf Protein-Macroarrays ermšglichten. Neben der transaktivierenden DomŠne (TAD) wurde die regulatorische Region in ihrer SUMOylierten und nicht-modifizierten Form in Screening-Experimenten eingesetzt. Eine Vielzahl von neuen C/EBPbeta-Bindungspartnern konnte identifiziert werden, wobei die konstitutive SUMOylierung C/EBPbeta-Interaktionen verŠndern kann. Bei den identifizierten Proteinen handelt es sich um Mitglieder der Polycomb Gruppe, Chromatin-modifizierende Enzyme, SignaltransduktionsmolekŸle und transkriptionelle Koregulatoren. Wissenschaftlich besonders interessant war die Identifikation der Lysin-Methyltransferase H3-K9-HMTase 3 (G9a) als Bindungspartner der transaktivierenden Region von C/EBPbeta. Diese Interaktion wurde durch GST-Bindungs- und KoimmunoprŠzipitationsstudien bestŠtigt. Durch massenspektrometrische Analysen konnte Monomethylierung der AminosŠuren K39 und K168 in C/EBPbeta nachgewiesen werden. Dadurch ergab sich die Vermutung, dass G9a nicht nur die Methylierung von Histon H3 katalysiert, sondern auch fŸr die Methylierung und Regulation von C/EBPbeta verantwortlich ist. Rekombinantes C/EBPbeta konnte durch G9a in vitro methyliert werden. Koexpression von C/EBPbeta und G9a fŸhrte zu einer Reduktion der transaktivierenden Eigenschaften von C/EBPbeta in AbhŠngigkeit von der katalytischen SET-DomŠne von G9a. Dieser Reduktion konnte durch Mutation der AminosŠuren K39 und K168 in Alanin entgegengewirkt werden. Als Bindungspartner der C/EBPbeta TAD konnte au§erdem die intrazellulŠre DomŠne von Notch1 (Notch1-ICD) identifiziert werden. Der Notch-Signalweg ist ein evolutionŠr konservierter Genschalter, der an vielen Entscheidungen in der Entwicklung sowie bei physiologischen und pathophysiologischen Prozessen im adulten Organismus, wie z. B. akuter lymphatischer T-Zell LeukŠmie (T-ALL), beteiligt ist. Die Interaktion zwischen Notch1-ICD und C/EBPbeta konnte in GST-Bindungsexperimenten und KoimmunoprŠzipitationsstudien verifiziert werden. In Reportergenstudien wurde eine Stimulation der C/EBPbeta-abhŠngigen Transkription durch Notch1-ICD beobachtet. C/EBPbeta ist demnach ein ZielmolekŸl des Notch1-Signalweges. / The transcription factor CCAAT/Enhancer Binding Protein beta (C/EBPbeta) regulates gene expression, proliferation and differentiation of various cell types. The function of C/EBPbeta is modulated by a number of co-factors which are components of macromolecular machines that alter the state of chromatin or that regulate gene transcription. Identification and functional characterisation of these co-factors is crucial for understanding the biology of C/EBPbeta. C/EBPbeta is regulated by a number of posttranslational modifications and can be found in phosphorylated, SUMOylated, acetylated and arginine- or lysine-methylated forms. SUMOylation of C/EBPbeta is considered a key modification which controls subsequent modifications. These modifications alter the gene regulatory functions of C/EBPbeta. C/EBPbeta binds two enzymes of the SUMOylation machinery, Ubc9 and PIAS3. This study shows that PIAS3 not only has E3-ligase activity during the SUMOylation of C/EBPbeta, but also interacts with SUMO-modified C/EBPbeta leading to repression of transcription. A proteome-wide screening procedure was established to identify novel interaction partners of C/EBPbeta. It was based on radioactively labelled proteins that can be utilized as probes to identify binding partners on solid phase protein-macroarrays. The C/EBPbeta transactivation domain (TAD) and its regulatory region in SUMOylated and non-SUMOylated form were used in different screening approaches. Using this procedure a number of novel C/EBPbeta interaction partners were identified, that depended in part on the SUMOylation status of C/EBPbeta. The major part of the C/EBPbeta-interacting proteins are members of the Polycomb group, chromatin-modifying enzymes, signal transduction molecules and transcriptional co-regulators. Interestingly, the lysine-methyltransferase H3-K9-HMTase 3 (G9a) was among the binding partners of C/EBPbeta that interacted with the TAD. This interaction was verified by GST-pulldown and co-immunoprecipitation studies. Mass spectrometrical analysis identified the amino acids K39 and K168 of C/EBPbeta to be mono-methylated. Therefore it was speculated that G9a not only catalyzes the methylation of Histone H3 but may also methylate and regulate C/EBPbeta. Indeed, recombinant C/EBPbeta could be methylated by G9a in vitro. Co-expression of C/EBPbeta and G9a resulted in a reduction of the transactivating potential of C/EBPbeta, which depended on the catalytical SET domain of G9a. This reduction could be counteracted by mutating the amino acids K39 and K168 to alanine. In addition to G9a, the Notch1 intracellular domain (Notch1-ICD) could also be identified as a novel binding partner of the C/EBPbeta TAD. Notch is a component of an evolutionary conserved pathway that acts on numerous physiological and pathophysiological processes during development and in the adult, e.g. T-cell acute lymphoblastic leukemia (T-ALL). The interaction between Notch1-ICD and C/EBPbeta could be verified in GST-pulldown studies and by co-immunoprecipitation. Reporter gene studies showed a stimulation of C/EBPbeta-dependent transcription through Notch1-ICD. C/EBPbeta is therefore a novel target molecule of the Notch1 signaling pathway.

Fatores de transcrição (FT) são proteínas que se ligam em sequências específicas e bem conservadas de nucleotídeos no DNA, denominadas sítios de ligação dos fatores de transcrição (SLFT), localizadas em regiões de regulação gênica conhecidas como módulos cis-reguladores (CRM). Ao reconhecer o SLFT, o fator de transcrição se liga naquele sítio e influencia a transcrição gênica positiva ou negativamente. Existem técnicas experimentais para a identificação dos locais dos SLFTs em um genoma, como footprinting, ChIP-chip ou ChIP-seq. Entretanto, a execução de tais técnicas implica em custos e tempo elevados. Alternativamente, pode-se utilizar as sequências de SLFTs já conhecidas para um determinado fator de transcrição e aplicar técnicas de aprendizado computacional supervisionado para criar um modelo computacional para tal sítio e então realizar a predição computacional no genoma. Entretanto, a maioria das ferramentas computacionais existentes para esse fim considera independência entre as posições entre os nucleotídeos de um sítio - como as baseadas em PWMs (position weight matrix) - o que não é necessariamente verdade. Este projeto teve como objetivo avaliar a utilização de gramáticas regulares estocásticas (GRE) como técnica alternativa às PWMs neste problema, uma vez que GREs são capazes de caracterizar dependências entre posições consecutivas dos sítios. Embora as diferenças de desempenho tenham sido sutis, GREs parecem mesmo ser mais adequadas do que PWMs na presença de valores mais altos de dependência de bases, e PWMs nos demais casos. Por fim, uma ferramenta de predição computacional de SLFTs foi criada baseada tanto em GREs quanto em PWMs. / Transcription factors (FT) are proteins that bind to specific and well-conserved sequences of nucleotides in the DNA, called transcription factor binding sites (TFBS), contained in regions of gene regulation known as cis-regulatory modules (CRM). By recognizing TFBA, the transcription factor binds to that site and positively or negatively influence the gene transcription. There are experimental procedures for the identification of TFBS in a genome such as footprinting, ChIP-chip or ChIP-Seq. However, the implementation of these techniques involves high costs and time. Alternatively, one may utilize the TFBS sequences already known for a particular transcription factor and applying computational supervised learning techniques to create a computational model for that site and then perform the computational prediction in the genome. However, most existing software tools for this purpose considers independence between nucleotide positions in the site - such as those based on PWMs (position weight matrix) - which is not necessarily true. This project aimed to evaluate the use of stochastic regular grammars (SRG) as an alternative technique to PWMs in this problem, since SRGs are able to characterize dependencies between consecutive positions in the sites. Although differences in performance have been subtle, SRGs appear to be more suitable than PWMs in the presence of higher base dependency values, and PWMs in other cases. Finally, a computational TFBS prediction tool was created based on both SRGs and PWMs.

No description available.

Le cancer du sein est la principale cause de mortalité par cancer chez la femme. Deux des facteurs de transcription de la famille Fos, Fra-1 et Fra-2, sont surexprimés dans les cancers du sein agressifs et contribuent au phénotype tumoral en favorisant entre autres, la prolifération, la motilité et l'invasivité. De façon surprenante, les mécanismes moléculaires via lesquels Fra-1 et Fra-2 (et plus généralement le complexe transcriptionnel AP-1 dont ils sont des constituants) gouvernent la transcription de leurs gènes cibles sont quasi-inconnus. Dans ce contexte, en combinant diverses approches (immunoprécipitation de chromatine, interférence à l'ARN…), j'ai étudié les mécanismes moléculaires par lesquels Fra-1 et Fra-2 contrôlent la transcription dérégulée du gène de l'urokinase ou uPA (sérine protéase cruciale dans la progression tumorale et l'établissement de métastases) qui est l'un des nouveaux marqueurs utilisés en clinique pour la mise en place des choix thérapeutiques. Mes travaux montrent de façon originale que (i) Fra-1 et Fra-2 agissent de façon non redondante et coopèrent pour réguler l'expression d'uPA via leur fixation sur un enhancer AP-1 localisé à -1,9 kb du site d'initiation de la transcription (TSS), (ii) Fra-2 est nécessaire au recrutement de RNA Pol II au niveau de l'enhancer, tandis que Fra-1 stimule le passage de RNA Pol II de sa forme initiatrice à sa forme élongatrice et (iii) que la polymérase recrutée à l'enhancer rejoint le TSS par un mécanisme de « tracking », très rarement décrit dans la littérature, en produisant de petits ARNs non codants, bidirectionnels et instables. / Breast cancer is the most frequent malignant disease among women. Two transcription factors, Fra-1 and Fra-2, belonging to the Fos family members, are overexpressed in aggressive breast cancers and contribute to the tumorigenic phenotype by favoring proliferation, motility and invasion. Surprisingly, the molecular mechanisms governed by Fra-1 and Fra-2 (and more generally by the AP-1 transcriptional complex, which they are components of) for the transcription of their target genes are still largely unknown. In this context, by combining different approaches (chromatin immunoprecipitation, RNA interference…), I studied the molecular mechanisms orchestrated by Fra-1 and Fra-2 for the expression of the urokinase (or uPA) gene (encoding a serine protease crucial for tumor progression and metastasis), which is one of the new diagnostic markers now taken into consideration for deciding therapeutic strategies. Interestingly, my results show that (i) Fra-1 and Fra-2 have non redundant functions and cooperate for the transcriptional regulation of uPA through their binding to AP-1 enhancer located 1.9 kb upstream of the transcriptional start site (TSS), (ii) Fra-2 is required for the recruitment of RNA Pol II on this enhancer while Fra-1 allows the conversion of RNA Pol II initiating form into its elongating form and (iii) enhancer-recruited RNA Pol II reaches the TSS by a tracking mechanism, mechanism very rarely described in the literature, during which it synthetizes small, unstable bidirectional, non coding RNAs.

Fatores de transcrição (FT) são proteínas que se ligam em sequências específicas e bem conservadas de nucleotídeos no DNA, denominadas sítios de ligação dos fatores de transcrição (SLFT), localizadas em regiões de regulação gênica conhecidas como módulos cis-reguladores (CRM). Ao reconhecer o SLFT, o fator de transcrição se liga naquele sítio e influencia a transcrição gênica positiva ou negativamente. Existem técnicas experimentais para a identificação dos locais dos SLFTs em um genoma, como footprinting, ChIP-chip ou ChIP-seq. Entretanto, a execução de tais técnicas implica em custos e tempo elevados. Alternativamente, pode-se utilizar as sequências de SLFTs já conhecidas para um determinado fator de transcrição e aplicar técnicas de aprendizado computacional supervisionado para criar um modelo computacional para tal sítio e então realizar a predição computacional no genoma. Entretanto, a maioria das ferramentas computacionais existentes para esse fim considera independência entre as posições entre os nucleotídeos de um sítio - como as baseadas em PWMs (position weight matrix) - o que não é necessariamente verdade. Este projeto teve como objetivo avaliar a utilização de gramáticas regulares estocásticas (GRE) como técnica alternativa às PWMs neste problema, uma vez que GREs são capazes de caracterizar dependências entre posições consecutivas dos sítios. Embora as diferenças de desempenho tenham sido sutis, GREs parecem mesmo ser mais adequadas do que PWMs na presença de valores mais altos de dependência de bases, e PWMs nos demais casos. Por fim, uma ferramenta de predição computacional de SLFTs foi criada baseada tanto em GREs quanto em PWMs. / Transcription factors (FT) are proteins that bind to specific and well-conserved sequences of nucleotides in the DNA, called transcription factor binding sites (TFBS), contained in regions of gene regulation known as cis-regulatory modules (CRM). By recognizing TFBA, the transcription factor binds to that site and positively or negatively influence the gene transcription. There are experimental procedures for the identification of TFBS in a genome such as footprinting, ChIP-chip or ChIP-Seq. However, the implementation of these techniques involves high costs and time. Alternatively, one may utilize the TFBS sequences already known for a particular transcription factor and applying computational supervised learning techniques to create a computational model for that site and then perform the computational prediction in the genome. However, most existing software tools for this purpose considers independence between nucleotide positions in the site - such as those based on PWMs (position weight matrix) - which is not necessarily true. This project aimed to evaluate the use of stochastic regular grammars (SRG) as an alternative technique to PWMs in this problem, since SRGs are able to characterize dependencies between consecutive positions in the sites. Although differences in performance have been subtle, SRGs appear to be more suitable than PWMs in the presence of higher base dependency values, and PWMs in other cases. Finally, a computational TFBS prediction tool was created based on both SRGs and PWMs.

The study examined interactions between morogo plants, arbuscular mycorrhizal fungi (AMF) and Fusarium species. Morogo refers to traditional leafy vegetables that, together with maize porridge, are dominant staple foods in rural areas of the Limpopo Province such as the Dikgale Demographic Surveillance Site (DDSS). Morogo plants grow either as weeds (often among maize), occur naturally in the field or are cultivated as subsistence crops by rural communities. Botanical species of morogo plants consumed in the DDSS were determined. Colonisation of morogo plant roots by AMF and Fusarium species composition in the immediate soil environment were investigated in four of eight DDSS subsistence communities, Isolated AMF were shown to belong to the genera Acaulospora and Glomus. Twelve Fusarium species were isolated from soil among which Fusariurn verticilliodes and Fusarium proliferaturn occurred predominantly. Greenhouse pot trials were conducted to examine the effect of AMF on morogo plant growth (cowpea; Mgna unguiculata) and Fusarium proliferatum levels in soil, Interaction between plants and AMF, as well as tripartite interactions of cowpea plants, AMF and Fusarium proliferatum were investigated. Non-inoculated cowpea plants served as controls for the following inoculations of cowpea in pots: (i) Fusarium proliferatum; (ii) commercial AMF from Mycoroot (PTY) Ltd. (a mixture of selected indigenous Glomus spp referred to commercial AMF for the purpose of this study); (iii) indigenous AMF obtained from DDSS soil (referred to iocal AMF for the purpose of this study); (iv) commercial AMF plus Fusarium proliferatum; (v) local AMF plus Fusariurn proliferatum. Results showed reduced root colonization by local as well as commercial AMF when Fusarium proliferatum were present. Local AMF significantly enhanced cowpea growth while commercial AMF apparently reduced the level of Fusarium proliferatum in the rhizosphere and surrounding soil. Results suggest that AMF may have potential as biological growth enhancers and bioprotective agents against Fusarium proliferatum. / Thesis (M. Environmental Science (Water Science))--North-West University, Potchefstroom Campus, 2007.

The goal of the Encyclopedia of DNA Elements (ENCODE) project has been to characterize all the functional elements of the human genome. These elements include expressed transcripts and genomic regions bound by transcription factors (TFs), occupied by nucleosomes, occupied by nucleosomes with modified histones, or hypersensitive to DNase I cleavage, etc. Chromatin Immunoprecipitation (ChIP-seq) is an experimental technique for detecting TF binding in living cells, and the genomic regions bound by TFs are called ChIP-seq peaks. ENCODE has performed and compiled results from tens of thousands of experiments, including ChIP-seq, DNase, RNA-seq and Hi-C. These efforts have culminated in two web-based resources from our lab—Factorbook and SCREEN—for the exploration of epigenomic data for both human and mouse. Factorbook is a peak-centric resource presenting data such as motif enrichment and histone modification profiles for transcription factor binding sites computed from ENCODE ChIP-seq data. SCREEN provides an encyclopedia of ~2 million regulatory elements, including promoters and enhancers, identified using ENCODE ChIP-seq and DNase data, with an extensive UI for searching and visualization. While we have successfully utilized the thousands of available ENCODE ChIP-seq experiments to build the Encyclopedia and visualizers, we have also struggled with the practical and theoretical inability to assay every possible experiment on every possible biosample under every conceivable biological scenario. We have used machine learning techniques to predict TF binding sites and enhancers location, and demonstrate machine learning is critical to help decipher functional regions of the genome.