Localization and distribution of estrogen receptors and progesterone receptors in the bovine ovary in relation to the cell dynamics

Tijdens de oestrische cyclus van het rund zijn de verschillende ovariële structuren onderworpen aan uitgebreide weefselveranderingen. Deze veranderingen bestaan uit opeenvolgende fasen van celproliferatie (mitose) en celdood (apoptose). Alhoewel de ovariële processen reeds uitvoerig bestudeerd zijn, blijven de regulatiemechanismen die verantwoordelijk zijn voor de cyclische veranderingen onduidelijk. De steroïdale geslachtshormonen oestro-geen en progesteron spelen hierbij een grote rol. Beide hormonen oefenen hun functie uit door te binden aan specifieke receptoren, de oestrogeenreceptoren en de progesteronreceptoren. De voornaamste doelstellingen van deze studie bestonden uit het lokaliseren van de oestrogeen- en de progesteronreceptoren in de verschillende ovariële celtypes van normaal cycle-rende runderen en het onderzoeken van mogelijke veranderingen in de receptordistributie tijdens de oestrische cyclus. Een volgende doelstelling was het nagaan van een mogelijke relatie tussen de aanwezigheid van de steroïdreceptoren en de cellulaire activiteit, met name proliferatie en apoptose. Een laatste objectief was het onderzoeken van de mogelijke correlatie tussen deze parameters en de progesteronconcentraties in het bloedplasma. In het eerste hoofdstuk werd de lokalisatie beschreven van oestrogeenreceptor-α (ERα) en oestrogeenreceptor-β mRNA (ERβ mRNA) in de verschillende ovariële celtypes gedurende de oestrische cyclus van het rund. Deze cyclus werd ingedeeld in oestrus, metoestrus, vroege dioestrus, late dioestrus en prooestrus. De score voor ERα was laag in de ovariële follikels, de tunica albuginea en het oppervlakte-epitheel, maar was hoger in de cellen van het diepe en oppervlakkige stroma. Daarentegen waren in alle folliculaire ovariële structuren de scores voor ERβ mRNA aanzienlijk hoger dan de scores voor ERα. In cellen van het corpus luteum werden cyclische schommelingen voor ERβ mRNA waargenomen. In het algemeen was er een lage en negatieve correlatie tussen de progesteronconcentraties in het bloed-plasma en de scores voor ERα en voor ERβ mRNA in alle ovariële celtypes. In een volgende studie werd de immunolokalisatie van de progesteronreceptor (PR) onderzocht in de verschillende ovariële celtypes tijdens de oestrische cyclus. De PR-expressie nam toe naarmate de follikel ontwikkelde. Vitale en cysteuze tertiaire follikels vertoonden hetzelfde expressiepatroon. In oblitererende follikels werd daarentegen een ander expressiepatroon van PR aangetroffen en was de PR score duidelijk lager dan in vitale en cysteuze follikels. Over het algemeen was tijdens de oestrus de score voor PR hoog in alle folliculaire structuren en daalde deze score gedurende de volgende stadia. Daarenboven was de correlatie tussen de PR-scores en de progesteron-concentraties in het bloedplasma negatief. Om de relatie te bestuderen tussen de distributie van ER en PR enerzijds en de cellulaire dynamiek anderzijds werden de proliferatie en apoptose nagegaan in de verschillende celtypes van de runderovaria. In het tweede hoofdstuk werd de celspecifieke lokalisatie beschreven van apoptose in het runderovarium tijdens de oestrische cyclus. Opmerkelijk in deze studie was dat in primordiale, primaire en secundaire follikels apoptose niet kon worden aangetoond. Daarentegen werden veel atretische tertiaire follikels waargenomen in alle runderovaria tijdens de verschillende cyclusstadia. Cysteuze follikels vertoonden hogere apoptotische scores in vergelijking met oblitererende follikels. De correlatie tussen apoptose en de progesteron-concentraties in het bloedplasma was laag. In het laatste hoofdstuk werd de proliferatieve activiteit in het runderovarium tijdens de verschillende ovariële stadia beschreven. Een opmerkelijke bevinding was het feit dat de proliferatieve activiteit van vitale tertiaire follikels aanhoudt in cysteuze follikels maar niet in oblitererende follikels. In corpora lutea was het cyclisch patroon verschillend van dat van de andere celtypes en werden hoge proliferatiewaarden gevonden tijdens metoestrus en vroege dioestrus. Nochtans was de correlatie tussen de proliferatie en de progesteronconcentraties in het bloedplasma laag. De uitgebreide distributie van oestrogeen- en progesteronreceptoren in de verschillende ovariële celtypes van het rund illustreert het belang van oestrogenen en progesteron in de fysiologie van het runderovarium. De topografische en nume-rieke gegevens bekomen uit deze studie kunnen van nut zijn voor verder onderzoek naar de specifieke rol van oestrogenen en progesteron in de regulatie van de ovariële activiteit

Ideal MHD theory of low-frequency Alfven waves in the H-1 Heliac

A part analytical, part numerical ideal MHD analysis of low-frequency Alfven wave physics in the H-1 stellarator is given. The three-dimensional, compressible ideal spectrum for H-1 is presented and it is found that despite the low beta (approx. 10^-4) of H-1 plasmas, significant Alfven-acoustic interactions occur at low frequencies. Several quasi-discrete modes are found with the three-dimensional linearised ideal MHD eigenmode solver CAS3D, including beta-induced Alfven eigenmode (BAE)- type modes in beta-induced gaps. The strongly shaped, low-aspect ratio magnetic geometry of H-1 causes CAS3D convergence difficulties requiring the inclusion of many Fourier harmonics for the parallel component of the fluid displacement eigenvector even for shear wave motions. The highest beta-induced gap reproduces large parts of the observed configurational frequency dependencies in the presence of hollow temperature profiles

Efficient algorithms for accurate hierarchical clustering of huge datasets: tackling the entire protein space

Motivation: UPGMA (average linking) is probably the most popular algorithm for hierarchical data clustering, especially in computational biology. However, UPGMA requires the entire dissimilarity matrix in memory. Due to this prohibitive requirement, UPGMA is not scalable to very large datasets

Bayesian hierarchical clustering for studying cancer gene expression data with unknown statistics

Clustering analysis is an important tool in studying gene expression data. The Bayesian hierarchical clustering (BHC) algorithm can automatically infer the number of clusters and uses Bayesian model selection to improve clustering quality. In this paper, we present an extension of the BHC algorithm. Our Gaussian BHC (GBHC) algorithm represents data as a mixture of Gaussian distributions. It uses normal-gamma distribution as a conjugate prior on the mean and precision of each of the Gaussian components. We tested GBHC over 11 cancer and 3 synthetic datasets. The results on cancer datasets show that in sample clustering, GBHC on average produces a clustering partition that is more concordant with the ground truth than those obtained from other commonly used algorithms. Furthermore, GBHC frequently infers the number of clusters that is often close to the ground truth. In gene clustering, GBHC also produces a clustering partition that is more biologically plausible than several other state-of-the-art methods. This suggests GBHC as an alternative tool for studying gene expression data. The implementation of GBHC is available at https://sites. google.com/site/gaussianbhc

Bioinformatics

Motivation: Theoretical efforts to understand the regulation of gene expression are traditionally centered around the identification of transcription factor binding sites at specific DNA positions. More recently these efforts have been supplemented by experimental data for relative binding affinities of proteins to longer intergenic sequences. The question arises to what extent these two approaches converge. In this paper, we adopt a physical binding model to predict the relative binding affinity of a transcription factor for a given sequence. Results: We find that a significant fraction of genome-wide binding data in yeast can be accounted for by simple count matrices and a physical model with only two parameters. We demonstrate that our approach is both conceptually and practically more powerful than traditional methods, which require selection of a cutoff. Our analysis yields biologically meaningful parameters, suitable for predicting relative binding affinities in the absence of experimental binding data. Availability: The C source code for our TRAP program is freely available for non-commercial use at http://www.molgen.mpg.de/~manke/papers/TFaffinities

Unraveling gene regulatory networks from time-resolved gene expression data -- a measures comparison study

Peer reviewedPublisher PD

RegPredict: an integrated system for regulon inference in prokaryotes by comparative genomics approach

RegPredict web server is designed to provide comparative genomics tools for reconstruction and analysis of microbial regulons using comparative genomics approach. The server allows the user to rapidly generate reference sets of regulons and regulatory motif profiles in a group of prokaryotic genomes. The new concept of a cluster of co-regulated orthologous operons allows the user to distribute the analysis of large regulons and to perform the comparative analysis of multiple clusters independently. Two major workflows currently implemented in RegPredict are: (i) regulon reconstruction for a known regulatory motif and (ii) ab initio inference of a novel regulon using several scenarios for the generation of starting gene sets. RegPredict provides a comprehensive collection of manually curated positional weight matrices of regulatory motifs. It is based on genomic sequences, ortholog and operon predictions from the MicrobesOnline. An interactive web interface of RegPredict integrates and presents diverse genomic and functional information about the candidate regulon members from several web resources. RegPredict is freely accessible at http://regpredict.lbl.gov

Proteomic Profiling of Burkholderia thailandensis During Host Infection Using Bio-Orthogonal Noncanonical Amino Acid Tagging (BONCAT)

Burkholderia pseudomallei and B. mallei are the causative agents of melioidosis and glanders, respectively, and are often fatal to humans and animals. Owing to the high fatality rate, potential for spread by aerosolization, and the lack of efficacious therapeutics, B. pseudomallei and B. mallei are considered biothreat agents of concern. In this study, we investigate the proteome of Burkholderia thailandensis, a closely related surrogate for the two more virulent Burkholderia species, during infection of host cells, and compare to that of B. thailandensis in culture. Studying the proteome of Burkholderia spp. during infection is expected to reveal molecular mechanisms of intracellular survival and host immune evasion; but proteomic profiling of Burkholderia during host infection is challenging. Proteomic analyses of host-associated bacteria are typically hindered by the overwhelming host protein content recovered from infected cultures. To address this problem, we have applied bio-orthogonal noncanonical amino acid tagging (BONCAT) to B. thailandensis, enabling the enrichment of newly expressed bacterial proteins from virtually any growth condition, including host cell infection. In this study, we show that B. thailandensis proteins were selectively labeled and efficiently enriched from infected host cells using BONCAT. We also demonstrate that this method can be used to label bacteria in situ by fluorescent tagging. Finally, we present a global proteomic profile of B. thailandensis as it infects host cells and a list of proteins that are differentially regulated in infection conditions as compared to bacterial monoculture. Among the identified proteins are quorum sensing regulated genes as well as homologs to previously identified virulence factors. This method provides a powerful tool to study the molecular processes during Burkholderia infection, a much-needed addition to the Burkholderia molecular toolbox

Parallel mutual information estimation for inferring gene regulatory networks on GPUs

<p>Abstract</p> <p>Background</p> <p>Mutual information is a measure of similarity between two variables. It has been widely used in various application domains including computational biology, machine learning, statistics, image processing, and financial computing. Previously used simple histogram based mutual information estimators lack the precision in quality compared to kernel based methods. The recently introduced B-spline function based mutual information estimation method is competitive to the kernel based methods in terms of quality but at a lower computational complexity.</p> <p>Results</p> <p>We present a new approach to accelerate the B-spline function based mutual information estimation algorithm with commodity graphics hardware. To derive an efficient mapping onto this type of architecture, we have used the Compute Unified Device Architecture (CUDA) programming model to design and implement a new parallel algorithm. Our implementation, called CUDA-MI, can achieve speedups of up to 82 using double precision on a single GPU compared to a multi-threaded implementation on a quad-core CPU for large microarray datasets. We have used the results obtained by CUDA-MI to infer gene regulatory networks (GRNs) from microarray data. The comparisons to existing methods including ARACNE and TINGe show that CUDA-MI produces GRNs of higher quality in less time.</p> <p>Conclusions</p> <p>CUDA-MI is publicly available open-source software, written in CUDA and C++ programming languages. It obtains significant speedup over sequential multi-threaded implementation by fully exploiting the compute capability of commonly used CUDA-enabled low-cost GPUs.</p