Alexander Kuhn

A global view of gene expression in lithium and zinc treated sea urchin embryos: new components of gene regulatory networks

BackgroundThe genome of the sea urchin Strongylocentrotus purpuratus has recently been sequenced because it is a major model system for the study of gene regulatory networks. Embryonic expression patterns for most genes are unknown, however.ResultsUsing large-scale screens on arrays carrying 50% to 70% of all genes, we identified novel territory-specific markers. Our strategy was based on computational selection of genes that are differentially expressed in lithium-treated embryos, which form excess endomesoderm, and in zinc-treated embryos, in which endomesoderm specification is blocked. Whole-mount in situ hybridization (WISH) analysis of 700 genes indicates that the apical organ region is eliminated in lithium-treated embryos. Conversely, apical and specifically neural markers are expressed more broadly in zinc-treated embryos, whereas endomesoderm signaling is severely reduced. Strikingly, the number of serotonergic neurons is amplified by at least tenfold in zinc-treated embryos. WISH analysis further indicates that there is crosstalk between the Wnt (wingless int), Notch, and fibroblast growth factor signaling pathways in secondary mesoderm cell specification and differentiation, similar to signaling cascades that function during development of presomitic mesoderm in mouse embryogenesis. We provide differential expression data for more than 4,000 genes and WISH patterns of more than 250 genes, and more than 2,400 annotated WISH images.ConclusionOur work provides tissue-specific expression patterns for a large fraction of the sea urchin genes that have not yet been included in existing regulatory networks and await functional integration. Furthermore, we noted neuron-inducing activity of zinc on embryonic development; this is the first observation of such activity in any organism.

Chordin is required for neural but not axial development in sea urchin embryos

The oral-aboral (OA) axis in the sea urchin is specified by the TGFbeta family members Nodal and BMP2/4. Nodal promotes oral specification, whereas BMP2/4, despite being expressed in the oral territory, is required for aboral specification. This study explores the role of Chordin (Chd) during sea urchin embryogenesis. Chd is a secreted BMP inhibitor that plays an important role in axial and neural specification and patterning in Drosophila and vertebrate embryos. In Lytechinus variegatus embryos, Chd and BMP2/4 are functionally antagonistic. Both are expressed in overlapping domains in the oral territory prior to and during gastrulation. Perturbation shows that, surprisingly, Chd is not involved in OA axis specification. Instead, Chd is required both for normal patterning of the ciliary band at the OA boundary and for development of synaptotagmin B-positive (synB) neurons in a manner that is reciprocal with BMP2/4. Chd expression and synB-positive neural development are both downstream from p38 MAPK and Nodal, but not Goosecoid. These data are summarized in a model for synB neural development.

Single-crystal X-ray structure analysis of the superionic conductor Li10GeP2S12

Tetragonal Li10GeP2S12 (LGPS) is the best solid Li ion conductor known to date. So far, the structure of the electrolyte was only determined from powder diffraction and Rietveld refinement. Here, we present the first single-crystal structure analysis of the tetragonal LGPS structure. The reported structure is largely verified. However, an additional Li position is clearly identified which might have a significant impact on the Li ion dynamics. All Li positions are partially occupied--a prerequisite for Li superionic conductors--and form a network of interconnected Li diffusion pathways. Therefore, we suggest that Li diffusion in this record solid electrolyte is less anisotropic than previously claimed.

On the origin of the chordate central nervous system: expression of onecut in the sea urchin embryo.

Summary We identified a transcription factor of the onecut class in the sea urchin Strongylocentrotus purpuratus that represents an ortholog of the mammalian gene HNF6, the founding member of the onecut class of proteins. The isolated sea urchin gene, named SpOnecut, encodes a protein of 483 amino acids with one cut domain and a homeodomain. Phylogenetic analysis clearly places the sea urchin gene into this family, most closely related to the ascidian onecut gene HNF‐6. Nevertheless, phylogenetic analysis reveals a difficult phylogeny indicating that certain members of the family evolve more rapidly than others and also that the cut domain and homeodomain evolve at a different pace. In fly, worm, ascidian, and teleost fish, the onecut genes isolated so far are exclusively expressed in cells of the central nervous system (CNS), whereas in mammals the two copies of the gene have acquired additional functions in liver and pancreas development. In the sea urchin embryo, expression is first detected in the emerging ciliary band at the late blastula stage. During the gastrula stage, expression is limited to the ciliary band. In the early pluteus stage, SpOnecut is expressed at the apical organ and the elongating arms but continues most prominently in the ciliary band. This is the first gene known that exclusively marks the ciliary band and therein the apical organ in a pluteus larva, whereas chordate orthologs execute essential functions in dorsal CNS development. The significance of this finding for the hypothesis that the ciliary bands and apical organs of the hypothetical “dipleurula”‐like chordate ancestor and the chordate/vertebrate CNS are of common origin is discussed.

High-Throughput miRNA and mRNA Sequencing of Paired Colorectal Normal, Tumor and Metastasis Tissues and Bioinformatic Modeling of miRNA-1 Therapeutic Applications

MiRNAs are discussed as diagnostic and therapeutic molecules. However, effective miRNA drug treatments with miRNAs are, so far, hampered by the complexity of the miRNA networks. To identify potential miRNA drugs in colorectal cancer, we profiled miRNA and mRNA expression in matching normal, tumor and metastasis tissues of eight patients by Illumina sequencing. We validated six miRNAs in a large tissue screen containing 16 additional tumor entities and identified miRNA-1, miRNA-129, miRNA-497 and miRNA-215 as constantly de-regulated within the majority of cancers. Of these, we investigated miRNA-1 as representative in a systems-biology simulation of cellular cancer models implemented in PyBioS and assessed the effects of depletion as well as overexpression in terms of miRNA-1 as a potential treatment option. In this system, miRNA-1 treatment reverted the disease phenotype with different effectiveness among the patients. Scoring the gene expression changes obtained through mRNA-Seq from the same patients we show that the combination of deep sequencing and systems biological modeling can help to identify patient-specific responses to miRNA treatments. We present this data as guideline for future pre-clinical assessments of new and personalized therapeutic options.

Touchless Optical Finger Motion Tracking Based on 2D Nanosheets with Giant Moisture Responsiveness

A new optical touchless positioning interface based on ultrasensitive humidity responsive 1D photonic crystals utilizing the giant moisture dependent swelling capacity of 2D phosphatoantimonate nanosheets is presented. The spatially confined, full spectral color change combined with reversible transparency switching induced by the humidity sheath of a human finger allows for real time, true color lateral finger motion tracking under touchless conditions.

Prediction in the face of uncertainty: A Monte Carlo-based approach for systems biology of cancer treatment

Cancer is known to be a complex disease and its therapy is difficult. Much information is available on molecules and pathways involved in cancer onset and progression and this data provides a valuable resource for the development of predictive computer models that can help to identify new potential drug targets or to improve therapies. Modeling cancer treatment has to take into account many cellular pathways usually leading to the construction of large mathematical models. The development of such models is complicated by the fact that relevant parameters are either completely unknown, or can at best be measured under highly artificial conditions. Here we propose an approach for constructing predictive models of such complex biological networks in the absence of accurate knowledge on parameter values, and apply this strategy to predict the effects of perturbations induced by anti-cancer drug target inhibitions on an epidermal growth factor (EGF) signaling network. The strategy is based on a Monte Carlo approach, in which the kinetic parameters are repeatedly sampled from specific probability distributions and used for multiple parallel simulations. Simulation results from different forms of the model (e.g., a model that expresses a certain mutation or mutation pattern or the treatment by a certain drug or drug combination) can be compared with the unperturbed control model and used for the prediction of the perturbation effects. This framework opens the way to experiment with complex biological networks in the computer, likely to save costs in drug development and to improve patient therapy.

Monte Carlo analysis of an ODE Model of the Sea Urchin Endomesoderm Network

BackgroundGene Regulatory Networks (GRNs) control the differentiation, specification and function of cells at the genomic level. The levels of interactions within large GRNs are of enormous depth and complexity. Details about many GRNs are emerging, but in most cases it is unknown to what extent they control a given process, i.e. the grade of completeness is uncertain. This uncertainty stems from limited experimental data, which is the main bottleneck for creating detailed dynamical models of cellular processes. Parameter estimation for each node is often infeasible for very large GRNs. We propose a method, based on random parameter estimations through Monte-Carlo simulations to measure completeness grades of GRNs.ResultsWe developed a heuristic to assess the completeness of large GRNs, using ODE simulations under different conditions and randomly sampled parameter sets to detect parameter-invariant effects of perturbations. To test this heuristic, we constructed the first ODE model of the whole sea urchin endomesoderm GRN, one of the best studied large GRNs. We find that nearly 48% of the parameter-invariant effects correspond with experimental data, which is 65% of the expected optimal agreement obtained from a submodel for which kinetic parameters were estimated and used for simulations. Randomized versions of the model reproduce only 23.5% of the experimental data.ConclusionThe method described in this paper enables an evaluation of network topologies of GRNs without requiring any parameter values. The benefit of this method is exemplified in the first mathematical analysis of the complete Endomesoderm Network Model. The predictions we provide deliver candidate nodes in the network that are likely to be erroneous or miss unknown connections, which may need additional experiments to improve the network topology. This mathematical model can serve as a scaffold for detailed and more realistic models. We propose that our method can be used to assess a completeness grade of any GRN. This could be especially useful for GRNs involved in human diseases, where often the amount of connectivity is unknown and/or many genes/interactions are missing.

A facile wet chemistry approach towards unilamellar tin sulfide nanosheets from Li4xSn1−xS2 solid solutions

We report on the facile production of single-layered tin sulfide nanosheets by a direct solid-state reaction, followed by quantitative liquid exfoliation in water. The new solid solution of SnS2 and Li2S with composition Li4xSn1−xS2 serves as a versatile solid-state precursor with tunable relative lithium and tin content. The end member Li2SnS3, corresponding to the solid solution composition Li3x[LixSn1−xS2], crystallizes in the well-known A2BO3 structure type with mixed Li/Sn layers alternating with pure Li layers in the cationic substructure, which is interleaved with sulfur layers. The bonding in the Li layers can be regarded as ionic, while the Sn–S bonds have substantial covalent character. The resulting inherent anisotropy allows for the facile production of unilamellar chalcogenide nanosheets with thicknesses below 1 nm and lateral sizes of tens of microns, simply by shaking the crystalline precursor in water. The quantitative exfoliation into single-layered nanosheets was confirmed using optical microscopy, AFM, TEM, as well as X-ray diffraction of freestanding films produced from the colloidal suspension by centrifugation. Upon annealing, the as-obtained nanosheets are converted into SnS2 without sacrificing their favorable dispersion properties in water. The presented method allows for the cheap and scalable production of unilamellar chalogenide nanosheets for various potential applications, such as in electronic devices, solar cells, sensors, or battery technology. We expect this method to be generic and transferable to the synthesis of other metal chalcogenides. The use of solid solutions as solid-state precursors, featuring a large compositional range and potential for doping with other metals, may ultimately allow for the controlled introduction of defect levels and rational band-gap engineering in nanosheet materials.

The “Virtual Patient” system: modeling cancer using deep sequencing technologies for personalized cancer treatment

Cancer is a complex disease involving many different genomic and epigenomic changes, mutations, copy number changes, loss of heterozygosity etc. These differences cause tumors with the same pathological classification to respond very differently to the drugs, making therapy decisions difficult. As a result of intensive research in the field of oncology much information is available on molecular interactions and pathways involved in cancer onset and progression. In addition recent increases in the capacity of next-generation sequencing systems will provide huge amounts of genome, epigenome and transcriptome data, making it feasible to apply deep sequencing in the clinic to characterize tumor/patient samples. Both, the complexity of disturbances in interaction networks of biological processes in cancer and the new molecular information generated by this sequence analysis urgently require the development of systems that are able to derive clinically relevant predictions from all available data. The “Virtual Patient” modeling system combines general information available about cancer relevant pathways with the individual (genome, transcriptome) information available on the individual tumor/patient to generate models able to predict the effects and side effects of individual drugs or drug combinations. This opens the way to experiment with the response of the model of the individual patient to different therapy options in the computer, offering new routes to improve oncological practice, reduce health costs but also to accelerate the development and the approval process for new drugs in this area.