SUMO pathway inhibition targets an aggressive pancreatic cancer subtype.

OBJECTIVE: Pancreatic ductal adenocarcinoma (PDAC) still carries a dismal prognosis with an overall 5-year survival rate of 9%. Conventional combination chemotherapies are a clear advance in the treatment of PDAC; however, subtypes of the disease exist, which exhibit extensive resistance to such therapies. Genomic MYC amplifications represent a distinct subset of PDAC with an aggressive tumour biology. It is clear that hyperactivation of MYC generates dependencies that can be exploited therapeutically. The aim of the study was to find and to target MYC-associated dependencies. DESIGN: We analysed human PDAC gene expression datasets. Results were corroborated by the analysis of the small ubiquitin-like modifier (SUMO) pathway in a large PDAC cohort using immunohistochemistry. A SUMO inhibitor was used and characterised using human and murine two-dimensional, organoid and in vivo models of PDAC. RESULTS: We observed that MYC is connected to the SUMOylation machinery in PDAC. Components of the SUMO pathway characterise a PDAC subtype with a dismal prognosis and we provide evidence that hyperactivation of MYC is connected to an increased sensitivity to pharmacological SUMO inhibition. CONCLUSION: SUMO inhibitor-based therapies should be further developed for an aggressive PDAC subtype.

Myeloid cell RelA/p65 promotes lung cancer proliferation through Wnt/ß-catenin signaling in murine and human tumor cells.

Smoking is the most important risk factor for both lung cancer (LC) and chronic obstructive pulmonary disease. The aim of this study was to investigate the role of myeloid cell nuclear factor-κB in the regulation of tumor cell growth signaling. We subjected mice lacking myeloid RelA/p65 (rela(Δ-/-)) to a metastatic LC model. Cigarette smoke (CS) exposure significantly increased the proliferation of Lewis lung carcinoma cell tumors in wild-type mice. In CS-exposed rela(Δ-/-) mice, the tumor growth was largely inhibited. Transcriptome and pathway analysis of cancer tissue revealed a fundamental impact of myeloid cells on various growth signaling pathways, including the Wnt/ß-catenin pathway. In conclusion, myeloid RelA/p65 is necessary to link smoke-induced inflammation with LC growth and has a role in the activation of Wnt/ß-catenin signaling in tumor cells.

Combining gene expression signatures and autoantibody profiles in human meningioma.

Gene expression profiling has emerged as powerful technique for studying the mechanisms of tumor genesis and development. Seroreactivity profiling of tumor antigens is a more recent technique that further contributes to the understanding of tumors and that offers itself for noninvasive tumor diagnosis. We performed expression profiling of 55,000 transcripts and expressed-sequence-tags for 24 meningiomas and related these data to autoantibody profiles of more than 50 antigens immunogenic in the autologous patients. The expression values of antigens in WHO grade I meningioma were significantly higher if the patients' sera reacted with these antigens as confirmed by a two-tailed Wilcoxon-Mann-Whitney test. Specifically, KIAA1344 that was previously identified as frequent antigen marker in meningioma, showed increased expression if antigens against KIAA1344 were detected in autologous patients. Our study is the first to combine genome-wide expression signatures and comprehensive seroreactivity patterns toward a more complete view on tumor immunology, especially concerning the overall role of the level of gene expression on the immunogenicity of meningioma antigens.

Development of a fluorescence-based assay for screening of modulators of human organic anion transporter 1B3 (OATP1B3).

The organic anion transporting protein 1B3 (OATP1B3), formerly termed OATP8, is responsible for uptake and subsequent elimination of multiple amphipathic drugs by the liver. In silico methods for the prediction of transport rates for drugs and drug-like molecules might provide an important tool in drug development. Most prediction methods however require a large training set of in vitro experimental data in order to yield reliable results. To obtain these data, we have developed a fluorescence-based assay that allows screening a relatively high number of substances for their transporter affinity. HEK293 cells overexpressing OATP1B3 (HEK-OATP8) [Y. Cui, J. Konig, D. Keppler, Vectorial transport by double-transfected cells expressing the human uptake transporter SLC21A8 and the apical export pump ABCC2, Mol. Pharmacol. 60 (2001) 934-943.] were tested for transport of Fluo-3. Fluo-3 uptake could be seen in a concentration-dependent manner. Uptake can be inhibited completely by the addition of the known OATP1B3-inhibitor rifampicin proving that Fluo-3 is transported by OATP1B3. To verify the suitability of the system to identify modulators of OATP1B3, we tested known substrates for competitively inhibiting the Fluo-3 transport by giving them simultaneously with a 2muM Fluo-3-solution to the cells. The transport of Fluo-3 was decreased by all test substrates in a concentration dependent manner.

Novel formulation of nonlocal electrostatics.

The accurate modeling of the dielectric properties of water is crucial for many applications in physics, computational chemistry, and molecular biology. This becomes possible in the framework of nonlocal electrostatics, for which we propose a novel formulation allowing for numerical solutions for the nontrivial molecular geometries arising in the applications mentioned before. Our approach is based on the introduction of a secondary field psi, which acts as the potential for the rotation free part of the dielectric displacement field D. For many relevant models, the dielectric function of the medium can be expressed as the Green's function of a local differential operator. In this case, the resulting coupled Poisson (-Boltzmann) equations for psi and the electrostatic potential phi reduce to a system of coupled partial differential equations. The approach is illustrated by its application to simple geometries.

BioMiner--modeling, analyzing, and visualizing biochemical pathways and networks.

MOTIVATION: Understanding the biochemistry of a newly sequenced organism is an essential task for post-genomic analysis. Since, however, genome and array data grow much faster than biochemical information, it is necessary to infer reactions by comparative analysis. No integrated and easy to use software tool for this purpose exists as yet. RESULTS: We present a new software system--BioMiner--for analyzing and visualizing biochemical pathways and networks. BioMiner is based on a new comprehensive, extensible and reusable data model--BioCore--which can be used to model biochemical pathways and networks. As a first application we present PathFinder, a new tool predicting biochemical pathways by comparing groups of related organisms based on sequence similarity. We successfully tested PathFinder with a number of experiments, e.g. the well studied glycolysis in bacteria. Additionally, an application called PathViewer for the visualization of metabolic networks is presented. PathViewer is the first application we are aware of which supports the graphical comparison of metabolic networks of different organisms. AVAILABILITY: http://www.zbi.uni-saarland.de/chair/projects/BioMiner SUPPLEMENTARY INFORMATION: Additional information on experimental results can be found on our web site.

Structure prediction of protein complexes by an NMR-based protein docking algorithm.

Protein docking algorithms can be used to study the driving forces and reaction mechanisms of docking processes. They are also able to speed up the lengthy process of experimental structure elucidation of protein complexes by proposing potential structures. In this paper, we are discussing a variant of the protein-protein docking problem, where the input consists of the tertiary structures of proteins A and B plus an unassigned one-dimensional 1H-NMR spectrum of the complex AB. We present a new scoring function for evaluating and ranking potential complex structures produced by a docking algorithm. The scoring function computes a 'theoretical' 1H-NMR spectrum for each tentative complex structure and subtracts the calculated spectrum from the experimental one. The absolute areas of the difference spectra are then used to rank the potential complex structures. In contrast to formerly published approaches (e.g. [Morelli et al. (2000) Biochemistry, 39, 2530-2537]) we do not use distance constraints (intermolecular NOE constraints). We have tested the approach with four protein complexes whose three-dimensional structures are stored in the PDB data bank (Bernstein et al., 1977) and whose 1H-NMR shift assignments are available from the BMRB database. The best result was obtained for an example, where all standard scoring functions failed completely. Here, our new scoring function achieved an almost perfect separation between good approximations of the true complex structure and false positives.

BALL--rapid software prototyping in computational molecular biology. Biochemicals Algorithms Library.

MOTIVATION: Rapid software prototyping can significantly reduce development times in the field of computational molecular biology and molecular modeling. Biochemical Algorithms Library (BALL) is an application framework in C++ that has been specifically designed for this purpose. RESULTS: BALL provides an extensive set of data structures as well as classes for molecular mechanics, advanced solvation methods, comparison and analysis of protein structures, file import/export, and visualization. BALL has been carefully designed to be robust, easy to use, and open to extensions. Especially its extensibility which results from an object-oriented and generic programming approach distinguishes it from other software packages. BALL is well suited to serve as a public repository for reliable data structures and algorithms. We show in an example that the implementation of complex methods is greatly simplified when using the data structures and functionality provided by BALL.

An exact solution for the segment-to-segment multiple sequence alignment problem.

MOTIVATION: In molecular biology, sequence alignment is a crucial tool in studying the structure and function of molecules, as well as the evolution of species. In the segment-to-segment variation of the multiple alignment problem, the input can be seen as a set of non-gapped segment pairs (diagonals). Given a weight function that assigns a weight score to every possible diagonal, the goal is to choose a consistent set of diagonals of maximum weight. We show that the segment-to-segment multiple alignment problem is equivalent to a novel formulation of the Maximum Trace problem: the Generalized Maximum Trace (GMT) problem. Solving this problem to optimality, therefore, may improve upon the previous greedy strategies that are used for solving the segment-to-segment multiple sequence alignment problem. We show that the GMT can be stated in terms of an integer linear program and then solve the integer linear program using methods from polyhedral combinatorics. This leads to a branch-and-cut algorithm for segment-to-segment multiple sequence alignment. RESULTS: We report on our first computational experiences with this novel method and show that the program is able to find optimal solutions for real-world test examples.

A polyhedral approach to RNA sequence structure alignment.

Ribonucleic acid (RNA) is a polymer composed of four bases denoted A, C, G, and U. It generally is a single-stranded molecule where the bases form hydrogen bonds within the same molecule leading to structure formation. In comparing different homologous RNA molecules it is important to consider both the base sequence and the structure of the molecules. Traditional alignment algorithms can only account for the sequence of bases, but not for the base pairings. Considering the structure leads to significant computational problems because of the dependencies introduced by the base pairings. In this paper we address the problem of optimally aligning a given RNA sequence of unknown structure to one of known sequence and structure. We phrase the problem as an integer linear program and then solve it using methods from polyhedral combinatorics. In our computational experiments we could solve large problem instances--23S ribosomal RNA with more than 1400 bases--a size intractable for former algorithms.