Georg Fuellen

The PluriNetWork: An Electronic Representation of the Network Underlying Pluripotency in Mouse, and Its Applications

Background Analysis of the mechanisms underlying pluripotency and reprogramming would benefit substantially from easy access to an electronic network of genes, proteins and mechanisms. Moreover, interpreting gene expression data needs to move beyond just the identification of the up-/downregulation of key genes and of overrepresented processes and pathways, towards clarifying the essential effects of the experiment in molecular terms. Methodology/Principal Findings We have assembled a network of 574 molecular interactions, stimulations and inhibitions, based on a collection of research data from 177 publications until June 2010, involving 274 mouse genes/proteins, all in a standard electronic format, enabling analyses by readily available software such as Cytoscape and its plugins. The network includes the core circuit of Oct4 (Pou5f1), Sox2 and Nanog, its periphery (such as Stat3, Klf4, Esrrb, and c-Myc), connections to upstream signaling pathways (such as Activin, WNT, FGF, BMP, Insulin, Notch and LIF), and epigenetic regulators as well as some other relevant genes/proteins, such as proteins involved in nuclear import/export. We describe the general properties of the network, as well as a Gene Ontology analysis of the genes included. We use several expression data sets to condense the network to a set of network links that are affected in the course of an experiment, yielding hypotheses about the underlying mechanisms. Conclusions/Significance We have initiated an electronic data repository that will be useful to understand pluripotency and to facilitate the interpretation of high-throughput data. To keep up with the growth of knowledge on the fundamental processes of pluripotency and reprogramming, we suggest to combine Wiki and social networking software towards a community curation system that is easy to use and flexible, and tailored to provide a benefit for the scientist, and to improve communication and exchange of research results. A PluriNetWork tutorial is available at http://www.ibima.med.uni-rostock.de/IBIMA/PluriNetWork/.

Sex steroids in Sjögren's syndrome.

The purpose of the review is to consider pathomechanisms of Sjögrens syndrome (SS), which could explain the female dominance (9:1), the most common age of onset (40-50 years) and targeting of the exocrine glands. Estrogens seem to specifically protect secretory glandular acinar cells against apoptosis whereas lack of estrogens during menopause and climacterium specifically leads to increased apoptosis of the exocrine secretory cells. Male gonads produce testosterone and convert it in exocrine glands to dihydrotesterosterone (DHT), which is anti-apoptotic and protects against acinar cell apoptosis. Estrogen-deficient women need to produce dehydroepiandrosterone (DHEA) in the adrenal glands and convert it to DHT in exocrine glands in a complex and branching reaction network in which individual enzymatic reactions are catalyzed in forward and backward directions by a myriad of different isoforms of steroidogenic enzymes. Tailoring DHT in peripheral tissues is much more complex and vulnerable in women than in men. In SS the intracrine steroidogenic enzyme machinery is deranged. These endo-/intracrine changes impair acinar remodeling due to impaired integrin α1β1 and integrin α2β1 expression so that the intercalated duct progenitor cells are unable to migrate to the acinar space, to differentiate to secretory acinar cells upon contact with laminin-111 and laminin-211 specifically found in the acinar basement membrane. The disarranged endo-/intracrine estrogen/androgen balance induces acinar cells to release microparticles and apoptotic bodies and to undergo apoptotis and/or anoikis. Membrane particles contain potential autoantigens recognized by T- (TCRs) and B-cell receptors (BCRs) and danger-associated molecular patterns (DAMPs) recognized by Toll-like receptors (TLRs). In membrane particles (or carrier-complexes) antigen/adjuvant complexes could stimulate professional antigen capturing, processing and presenting cells, which can initiate auto-inflammatory and autoimmune cascades, break the self-tolerance and finally lead to SS.

A review of bioinformatics education in Germany

We describe the establishment of bioinformatics in Germany and give an overview of current bioinformatics education in this country, from the perspective of the practitioner. The aim of this study is to demonstrate development of a strong bioinformatics education at German universities and research institutes during the last years. Beginning with a definition of the multi-disciplinary field bioinformatics, we give a survey of government initiatives in Germany in support of this field, which resulted in a wide spectrum of courses. To the best of our knowledge, we compile all ongoing courses at universities and research institutes. Five case studies featuring university courses with different educational focus illustrate the variety of efforts. In this context we also discuss the main components of German bioinformatics curricula. These components can be considered as the basic knowledge of German bioinformaticians. We conclude by giving perspectives for further development of bioinformatics education.

The multikinase inhibitor Sorafenib displays significant antiproliferative effects and induces apoptosis via caspase 3, 7 and PARP in B- and T-lymphoblastic cells

BackgroundTargeted therapy approaches have been successfully introduced into the treatment of several cancers. The multikinase inhibitor Sorafenib has antitumor activity in solid tumors and its effects on acute lymphoblastic leukemia (ALL) cells are still unclear.MethodsALL cell lines (SEM, RS4;11 and Jurkat) were treated with Sorafenib alone or in combination with cytarabine, doxorubicin or RAD001. Cell count, apoptosis and necrosis rates, cell cycle distribution, protein phosphorylation and metabolic activity were determined.ResultsSorafenib inhibited the proliferation of ALL cells by cell cycle arrest accompanied by down-regulation of CyclinD3 and CDK4. Furthermore, Sorafenib initiated apoptosis by cleavage of caspases 3, 7 and PARP. Apoptosis and necrosis rates increased significantly with most pronounced effects after 96 h. Antiproliferative effects of Sorafenib were associated with a decreased phosphorylation of Akt (Ser473 and Thr308), FoxO3A (Thr32) and 4EBP-1 (Ser65 and Thr70) as early as 0.5 h after treatment. Synergistic effects were seen when Sorafenib was combined with other cytotoxic drugs or a mTOR inhibitor emphasizing the Sorafenib effect.ConclusionSorafenib displays significant antileukemic activity in vitro by inducing cell cycle arrest and apoptosis. Furthermore, it influences PI3K/Akt/mTOR signaling in ALL cells.

The Intrinsic Connectome of the Rat Amygdala

The connectomes of nervous systems or parts there of are becoming important subjects of study as the amount of connectivity data increases. Because most tract-tracing studies are performed on the rat, we conducted a comprehensive analysis of the amygdala connectome of this species resulting in a meta-study. The data were imported into the neuroVIISAS system, where regions of the connectome are organized in a controlled ontology and network analysis can be performed. A weighted digraph represents the bilateral intrinsic (connections of regions of the amygdala) and extrinsic (connections of regions of the amygdala to non-amygdaloid regions) connectome of the amygdala. Its structure as well as its local and global network parameters depend on the arrangement of neuronal entities in the ontology. The intrinsic amygdala connectome is a small-world and scale-free network. The anterior cortical nucleus (72 in- and out-going edges), the posterior nucleus (45), and the anterior basomedial nucleus (44) are the nuclear regions that posses most in- and outdegrees. The posterior nucleus turns out to be the most important nucleus of the intrinsic amygdala network since its Shapley rate is minimal. Within the intrinsic amygdala, regions were determined that are essential for network integrity. These regions are important for behavioral (processing of emotions and motivation) and functional (memory) performances of the amygdala as reported in other studies.

Nuclear Reprogramming: Kinetics of Cell Cycle and Metabolic Progression as Determinants of Success

Establishment of totipotency after somatic cell nuclear transfer (NT) requires not only reprogramming of gene expression, but also conversion of the cell cycle from quiescence to the precisely timed sequence of embryonic cleavage. Inadequate adaptation of the somatic nucleus to the embryonic cell cycle regime may lay the foundation for NT embryo failure and their reported lower cell counts. We combined bright field and fluorescence imaging of histone H2b-GFP expressing mouse embryos, to record cell divisions up to the blastocyst stage. This allowed us to quantitatively analyze cleavage kinetics of cloned embryos and revealed an extended and inconstant duration of the second and third cell cycles compared to fertilized controls generated by intracytoplasmic sperm injection (ICSI). Compared to fertilized embryos, slow and fast cleaving NT embryos presented similar rates of errors in M phase, but were considerably less tolerant to mitotic errors and underwent cleavage arrest. Although NT embryos vary substantially in their speed of cell cycle progression, transcriptome analysis did not detect systematic differences between fast and slow NT embryos. Profiling of amino acid turnover during pre-implantation development revealed that NT embryos consume lower amounts of amino acids, in particular arginine, than fertilized embryos until morula stage. An increased arginine supplementation enhanced development to blastocyst and increased embryo cell numbers. We conclude that a cell cycle delay, which is independent of pluripotency marker reactivation, and metabolic restraints reduce cell counts of NT embryos and impede their development.

Learning Biomarkers of Pluripotent Stem Cells in Mouse

Pluripotent stem cells are able to self-renew, and to differentiate into all adult cell types. Many studies report data describing these cells, and characterize them in molecular terms. Machine learning yields classifiers that can accurately identify pluripotent stem cells, but there is a lack of studies yielding minimal sets of best biomarkers (genes/features). We assembled gene expression data of pluripotent stem cells and non-pluripotent cells from the mouse. After normalization and filtering, we applied machine learning, classifying samples into pluripotent and non-pluripotent with high cross-validated accuracy. Furthermore, to identify minimal sets of best biomarkers, we used three methods: information gain, random forests and a wrapper of genetic algorithm and support vector machine (GA/SVM). We demonstrate that the GA/SVM biomarkers work best in combination with each other; pathway and enrichment analyses show that they cover the widest variety of processes implicated in pluripotency. The GA/SVM wrapper yields best biomarkers, no matter which classification method is used. The consensus best biomarker based on the three methods is Tet1, implicated in pluripotency just recently. The best biomarker based on the GA/SVM wrapper approach alone is Fam134b, possibly a missing link between pluripotency and some standard surface markers of unknown function processed by the Golgi apparatus.

VisCoSe: visualization and comparison of consensus sequences

We introduce visualization and comparison of consensus sequences (VisCoSe) as a WWW service and a stand-alone command line Perl script for visualizing and comparing consensus sequences of protein and nucleotide sequences. VisCoSe is the only interface available that simultaneously calculates consensus sequences of multiple data sets and automatically compares these consensus sequences. Furthermore, VisCoSe allows visualization of chemical properties of amino acids.

Epitope Predictions Indicate the Presence of Two Distinct Types of Epitope-Antibody-Reactivities Determined by Epitope Profiling of Intravenous Immunoglobulins

Epitope-antibody-reactivities (EAR) of intravenous immunoglobulins (IVIGs) determined for 75,534 peptides by microarray analysis demonstrate that roughly 9% of peptides derived from 870 different human protein sequences react with antibodies present in IVIG. Computational prediction of linear B cell epitopes was conducted using machine learning with an ensemble of classifiers in combination with position weight matrix (PWM) analysis. Machine learning slightly outperformed PWM with area under the curve (AUC) of 0.884 vs. 0.849. Two different types of epitope-antibody recognition-modes (Type I EAR and Type II EAR) were found. Peptides of Type I EAR are high in tyrosine, tryptophan and phenylalanine, and low in asparagine, glutamine and glutamic acid residues, whereas for peptides of Type II EAR it is the other way around. Representative crystal structures present in the Protein Data Bank (PDB) of Type I EAR are PDB 1TZI and PDB 2DD8, while PDB 2FD6 and 2J4W are typical for Type II EAR. Type I EAR peptides share predicted propensities for being presented by MHC class I and class II complexes. The latter interaction possibly favors T cell-dependent antibody responses including IgG class switching. Peptides of Type II EAR are predicted not to be preferentially presented by MHC complexes, thus implying the involvement of T cell-independent IgG class switch mechanisms. The high extent of IgG immunoglobulin reactivity with human peptides implies that circulating IgG molecules are prone to bind to human protein/peptide structures under non-pathological, non-inflammatory conditions. A webserver for predicting EAR of peptide sequences is available at www.sysmed-immun.eu/EAR.

PodNet, a protein–protein interaction network of the podocyte

Interactions between proteins crucially determine cellular structure and function. Differential analysis of the interactome may help elucidate molecular mechanisms during disease development; however, this analysis necessitates mapping of expression data on protein-protein interaction networks. These networks do not exist for the podocyte; therefore, we built PodNet, a literature-based mouse podocyte network in Cytoscape format. Using database protein-protein interactions, we expanded PodNet to XPodNet with enhanced connectivity. In order to test the performance of XPodNet in differential interactome analysis, we examined podocyte developmental differentiation and the effect of cell culture. Transcriptomes of podocytes in 10 different states were mapped on XPodNet and analyzed with the Cytoscape plugin ExprEssence, based on the law of mass action. Interactions between slit diaphragm proteins are most significantly upregulated during podocyte development and most significantly downregulated in culture. On the other hand, our analysis revealed that interactions lost during podocyte differentiation are not regained in culture, suggesting a loss rather than a reversal of differentiation for podocytes in culture. Thus, we have developed PodNet as a valuable tool for differential interactome analysis in podocytes, and we have identified established and unexplored regulated interactions in developing and cultured podocytes.