Analysis of protective cytoskeletal reactions during degenerative processes in the brain - extended characterization of Drebrin-deficient cells

Das Aktinfilament-bindende Protein Drebrin, welches bei Alzheimer Patienten vermindert vorliegt, ist ein wichtiger Stabilisator des Aktinnetzwerks in dendritischen Spines exzitatorischer Neurone (Harigaya et al. 1996; Hayashi et al. 1996). Obwohl ein Drebrin-Verlust allein keine Neurodegeneration auslöst (Willmes et al. 2017), wirkt die Drebrin-vermittelte Stabilisierung von Aktinfilamenten einer stressbedingten Neurodegeneration entgegen (Kreis et al. 2019). Eine nachlassende Reduktion von ROS könnte bei Alterungsprozessen in Neuronen eine wesentliche Rolle spielen (Mack et al. 2016). Es wurde daher untersucht, ob Drebrin-defiziente Neurone vulnerabler gegenüber altersinduzierten Stressfaktoren sind und ob Funktionseinschränkungen der ROS-Abwehr diese Vulnerabilität steigern. Dafür wurden Gehirnschnitte von adulten sowie dem Greisenalter entsprechenden DBN-KO Mäusen immunhistochemisch untersucht. Verglichen wurde dabei der Einfluss des Drebringens kombiniert mit einem partiellen KO der Gluatathion Peroxidase 4 (GPX4). In stark gealterten DBN-KO sowie in den DBN-KO/GPX4-Het Tieren zeigten sich gegenüber den Kontrolltieren keine Anzeichen für eine lokale Neurodegeneration in Hippocampus und Kortex, jedoch ein selektiver Verlust von NeuN in hippocampalen Pyramidalzellen. Weitere Untersuchungen zu möglichen subtilen Veränderungen aufgrund extremer Alterung konnten im Rahmen dieser Arbeit nicht mehr durchgeführt werden. Drebrin-defiziente Mäuse leiden zudem in Folge von Gehirnverletzungen unter einer fehlerhaften astrozytären Narbenbildung bedingt durch gestörten endosomalen Transport (Schiweck et al. 2021). Endosomaler Transport entscheidet vermutlich auch über die potentielle Neutralisation von Aβ-Peptiden, weshalb eine Rolle von Astrozyten in der Pathogenese von Morbus Alzheimer (AD) diskutiert wird (Prasad und Rao 2018). Um die Aufnahme von Aβ-Isoformen in Astrozyten zu analysieren, wurden astrozytäre Zellkulturen mit markierten Aβ-Monomeren/-Oligomeren behandelt und deren Aufnahme quantifiziert. Überdies wurden Untersuchungen zur Aβ-Aufnahme auf ein in-vitro-Wundheilungsmodell für Astrozytenkulturen ausgedehnt. Zusammenfassend lässt sich sagen, dass Astrozyten in Primärkultur ein heterogenes Aufnahmeverhalten beider Aβ-Isoformen zeigen. Allerdings konnte zum ersten Mal gezeigt werden, dass eine Subpopulation von Astrozyten (sog. „Vielfresser“) die Aβ-Monomere/-Oligomere in endosomale Vesikel aufnahmen und sich Nukleus-nahe Akkumulationen des Amyloids bildeten. Die Aufnahme der oligomeren Isoform wurde zudem durch den AD-Risikofaktor ApoE gesteigert. Diese Ergebnisse können dazu beitragen, die beobachteten Subpopulationen von Astrozyten anhand ihrer Aufnahme von Aβ-Isoformen gezielt zu charakterisieren. Zeitrafferstudien sowie eine genaue Analyse der Akkumulation innerhalb der Vielfresser könnten erste Hinweise auf mechanistische Zusammenhänge zwischen dem intrazellulären Schicksal des Aβ und seinem Schädigungspotential in Astrozyten aufdecken.The actin-side-binding protein Drebrin, of which lower levels were seen in AD patients, has been identified as an important stabilizer of the actin network in dendritic spines of excitatory neurons (Harigaya et al. 1996; Hayashi et al. 1996). Although a Drebrin loss on its own does not cause neurodegeneration in a mouse model (Willmes et al. 2017), Drebrin-mediated stabilization of actin filaments protects against insult-driven neurodegeneration (Kreis et al. 2019). Decreased reduction of ROS potentially causing a variety of cellular malfunctions could be one underlaying mechanism of age-induced stress in neurons (Mack et al. 2016). It was therefore analysed, whether Drebrin-deficient neurons are more vulnerable to age-dependent stressors and if impaired ROS-protection may increase this vulnerability. Therefore, brain sections ranging from adult to very old age were immunhistochemically evaluated with emphasis on the impact of Drebrin genotype in combination with a partial knock-down of the glutathione peroxidase 4 (GPX4). Whilst neither senile DBN-KO nore DBN-KO/GPX4-Het animals showed evidence of manifest neurodegeneration in the hippocampus or cortex, some pyramidal neurons of the hippocampus lost their common marker NeuN. Additional studies into more subtle transformations due to very old age in both mouse models were precluded by time constrains. Furthermore, Drebrin-deficient suffer from deficient astrocytic scar formation after brain injury due to failed endosomal transport (Schiweck et al. 2021). Endosomal transport may also plays a crucial role during neutralization of amyloidgenic Aβ-peptides, placing astrocytes in the centre stage of Alzheimer’s Disease (AD) pathogenesis (Prasad and Rao 2018). To directly analyse the intracellular uptake of Aβ-isoforms astrocytic cultures were treated with fluorescently labelled Aβ-monomers/oligomers and vesicular uptake per cell was quantified. In addition, an in vitro-scar formation assay was employed to activate endosomal turnover. In conclusion, astrocytes in culture show a remarkable range of Aβ-isoform uptake behaviour preventing significant differences between analysed parameters. However, a small subpopulation of astrocytes (termed “binge eaters”) took up Aβ-monomers as well as presumably toxic Aβ-oligomers in an extraordinary high amount of endosomal vesicles and at the same time displayed peri-nuclear accumulations of amyloid. Moreover, uptake in particular pf oligomeric Aβ was increased by ApoE, a well known risk factor for AD. Expanding these preliminary results, different subpopulations of astrocytes may be characterized on the basis of their labelled amyloid uptake. Hence, time lapse studies as well as more detailed analysis of endosomal Aβ accumulation in binge eaters may lead to first insights into mechanistic aspects of the intracellular fate of Aβ and its toxic potential in astrocytes

Mayday - integrative analytics for expression data

<p>Abstract</p> <p>Background</p> <p>DNA Microarrays have become the standard method for large scale analyses of gene expression and epigenomics. The increasing complexity and inherent noisiness of the generated data makes visual data exploration ever more important. Fast deployment of new methods as well as a combination of predefined, easy to apply methods with programmer's access to the data are important requirements for any analysis framework. Mayday is an open source platform with emphasis on visual data exploration and analysis. Many built-in methods for clustering, machine learning and classification are provided for dissecting complex datasets. Plugins can easily be written to extend Mayday's functionality in a large number of ways. As Java program, Mayday is platform-independent and can be used as Java WebStart application without any installation. Mayday can import data from several file formats, database connectivity is included for efficient data organization. Numerous interactive visualization tools, including box plots, profile plots, principal component plots and a heatmap are available, can be enhanced with metadata and exported as publication quality vector files.</p> <p>Results</p> <p>We have rewritten large parts of Mayday's core to make it more efficient and ready for future developments. Among the large number of new plugins are an automated processing framework, dynamic filtering, new and efficient clustering methods, a machine learning module and database connectivity. Extensive manual data analysis can be done using an inbuilt R terminal and an integrated SQL querying interface. Our visualization framework has become more powerful, new plot types have been added and existing plots improved.</p> <p>Conclusions</p> <p>We present a major extension of Mayday, a very versatile open-source framework for efficient micro array data analysis designed for biologists and bioinformaticians. Most everyday tasks are already covered. The large number of available plugins as well as the extension possibilities using compiled plugins and ad-hoc scripting allow for the rapid adaption of Mayday also to very specialized data exploration. Mayday is available at <url>http://microarray-analysis.org</url>.</p

GenomeRing: alignment visualization based on SuperGenome coordinates

Motivation: The number of completely sequenced genomes is continuously rising, allowing for comparative analyses of genomic variation. Such analyses are often based on whole-genome alignments to elucidate structural differences arising from insertions, deletions or from rearrangement events. Computational tools that can visualize genome alignments in a meaningful manner are needed to help researchers gain new insights into the underlying data. Such visualizations typically are either realized in a linear fashion as in genome browsers or by using a circular approach, where relationships between genomic regions are indicated by arcs. Both methods allow for the integration of additional information such as experimental data or annotations. However, providing a visualization that still allows for a quick and comprehensive interpretation of all important genomic variations together with various supplemental data, which may be highly heterogeneous, remains a challenge

iHAT: interactive Hierarchical Aggregation Table for Genetic Association Data

In the search for single-nucleotide polymorphisms which influence the observable phenotype, genome wide association studies have become an important technique for the identification of associations between genotype and phenotype of a diverse set of sequence-based data. We present a methodology for the visual assessment of single-nucleotide polymorphisms using interactive hierarchical aggregation techniques combined with methods known from traditional sequence browsers and cluster heatmaps. Our tool, the interactive Hierarchical Aggregation Table (iHAT), facilitates the visualization of multiple sequence alignments, associated metadata, and hierarchical clusterings. Different color maps and aggregation strategies as well as filtering options support the user in finding correlations between sequences and metadata. Similar to other visualizations such as parallel coordinates or heatmaps, iHAT relies on the human pattern-recognition ability for spotting patterns that might indicate correlation or anticorrelation. We demonstrate iHAT using artificial and real-world datasets for DNA and protein association studies as well as expression Quantitative Trait Locus data

Detection of viable Mycobacterium ulcerans in clinical samples by a novel combined 16S rRNA reverse transcriptase/IS2404 real-time qPCR assay.

Detection of Viable <em>Mycobacterium ulcerans</em> in Clinical Samples by a Novel Combined 16S rRNA Reverse Transcriptase/IS<em>2404</em> Real-Time qPCR Assa

Visualizing dimensionality reduction of systems biology data

One of the challenges in analyzing high-dimensional expression data is the detection of important biological signals. A common approach is to apply a dimension reduction method, such as principal component analysis. Typically, after application of such a method the data is projected and visualized in the new coordinate system, using scatter plots or profile plots. These methods provide good results if the data have certain properties which become visible in the new coordinate system and which were hard to detect in the original coordinate system. Often however, the application of only one method does not suffice to capture all important signals. Therefore several methods addressing different aspects of the data need to be applied. We have developed a framework for linear and non-linear dimension reduction methods within our visual analytics pipeline SpRay. This includes measures that assist the interpretation of the factorization result. Different visualizations of these measures can be combined with functional annotations that support the interpretation of the results. We show an application to high-resolution time series microarray data in the antibiotic-producing organism Streptomyces coelicolor as well as to microarray data measuring expression of cells with normal karyotype and cells with trisomies of human chromosomes 13 and 21

Mayday SeaSight: Combined Analysis of Deep Sequencing and Microarray Data

Recently emerged deep sequencing technologies offer new high-throughput methods to quantify gene expression, epigenetic modifications and DNA-protein binding. From a computational point of view, the data is very different from that produced by the already established microarray technology, providing a new perspective on the samples under study and complementing microarray gene expression data. Software offering the integrated analysis of data from different technologies is of growing importance as new data emerge in systems biology studies. Mayday is an extensible platform for visual data exploration and interactive analysis and provides many methods for dissecting complex transcriptome datasets. We present Mayday SeaSight, an extension that allows to integrate data from different platforms such as deep sequencing and microarrays. It offers methods for computing expression values from mapped reads and raw microarray data, background correction and normalization and linking microarray probes to genomic coordinates. It is now possible to use Mayday's wealth of methods to analyze sequencing data and to combine data from different technologies in one analysis

An eQTL biological data visualization challenge and approaches from the visualization community

In 2011, the IEEE VisWeek conferences inaugurated a symposium on Biological Data Visualization. Like other domain-oriented Vis symposia, this symposium's purpose was to explore the unique characteristics and requirements of visualization within the domain, and to enhance both the Visualization and Bio/Life-Sciences communities by pushing Biological data sets and domain understanding into the Visualization community, and well-informed Visualization solutions back to the Biological community. Amongst several other activities, the BioVis symposium created a data analysis and visualization contest. Unlike many contests in other venues, where the purpose is primarily to allow entrants to demonstrate tour-de-force programming skills on sample problems with known solutions, the BioVis contest was intended to whet the participants' appetites for a tremendously challenging biological domain, and simultaneously produce viable tools for a biological grand challenge domain with no extant solutions. For this purpose expression Quantitative Trait Locus (eQTL) data analysis was selected. In the BioVis 2011 contest, we provided contestants with a synthetic eQTL data set containing real biological variation, as well as a spiked-in gene expression interaction network influenced by single nucleotide polymorphism (SNP) DNA variation and a hypothetical disease model. Contestants were asked to elucidate the pattern of SNPs and interactions that predicted an individual's disease state. 9 teams competed in the contest using a mixture of methods, some analytical and others through visual exploratory methods. Independent panels of visualization and biological experts judged entries. Awards were given for each panel's favorite entry, and an overall best entry agreed upon by both panels. Three special mention awards were given for particularly innovative and useful aspects of those entries. And further recognition was given to entries that correctly answered a bonus question about how a proposed "gene therapy" change to a SNP might change an individual's disease status, which served as a calibration for each approaches' applicability to a typical domain question. In the future, BioVis will continue the data analysis and visualization contest, maintaining the philosophy of providing new challenging questions in open-ended and dramatically underserved Bio/Life Sciences domains