Organocatalytic Asymmetric Synthesis of Si-Stereogenic Silyl Ethers.

Functionalized enantiopure organosilanes are important building blocks with applications in various fields of chemistry; nevertheless, asymmetric synthetic methods for their preparation are rare. Here we report the first organocatalytic enantioselective synthesis of tertiary silyl ethers possessing "central chirality" on silicon. The reaction proceeds via a desymmetrizing carbon-carbon bond forming silicon-hydrogen exchange reaction of symmetrical bis(methallyl)silanes with phenols using newly developed imidodiphosphorimidate (IDPi) catalysts. A variety of enantiopure silyl ethers was obtained in high yields with good chemo- and enantioselectivities and could be readily derivatized to several useful chiral silicon compounds, leveraging the olefin functionality and the leaving group nature of the phenoxy substituent.

The benefits of Cu Kß radiation for the single-crystal X-ray structure determination of crystalline sponges.

The crystalline sponge method facilitates the X-ray structure determination of samples that do not crystallize or are too sparsely available to afford viable crystallization. By including these materials in a metal-organic framework, the structure of the guest molecules can be determined. Some of the inherent difficulties of this method are discussed and the use of Cu Kß radiation is presented as a simple and effective means to improve the quality of the diffraction data that can be obtained from a sponge crystal.

Metabolic Profiling of S-praziquantel: Structure Elucidation Using the Crystalline Sponge Method in Combination with Mass Spectrometry and Nuclear Magnetic Resonance.

Praziquantel (PZQ) is the drug of choice for treatment of the neglected tropical disease schistosomiasis. Although the drug has been extensively used over several decades and its metabolism well studied (several oxidative metabolites are known from literature), the knowledge of the complete structure of some of its metabolites remains elusive. Conventional techniques, such as nuclear magnetic resonance or liquid chromatography mass spectrometry were used in the past to investigate phase I and phase II metabolites of PZQ. These techniques are either limited to provide the complete molecular structure (liquid chromatography mass spectrometry) or require large amount of sample material (NMR), which are not always available when in vitro systems are used for investigation of the metabolites. In this study, we describe new structures of S-PZQ metabolites generated in vitro from human liver microsomes using the crystalline sponge method. After chromatographic separation and purification of the oxidative metabolites, ultra-performance liquid chromatography-quadrupole time-of-flight mass spectrometry analysis was conducted to narrow down the position of oxidation to a certain part of the molecule. To determine the exact position of hydroxylation, singe-crystal X-ray diffraction analysis of the crystalline sponges and absorbed analyte was used to identify the structure of S-PZQ and its metabolites. The crystalline sponge method allowed for complete structure elucidation of the known metabolites S-trans-4'-hydroxy-PZQ (M1), S-cis-4'-hydroxy-PZQ (M2) and S-/R-11b-hydroxy-PZQ (M6) as well as the unknown metabolites S-9-hydroxy-PZQ (M3) and S-7-hydroxy-S-PZQ (M4). For comparison of structural elucidation techniques, one metabolite (M3) was additionally analyzed using NMR. SIGNIFICANCE STATEMENT: The information content of the metabolic pathway of praziquantel is still limited. The crystalline sponge method allowed the complete structural elucidation of three known and two unknown metabolites of S-praziquantel, using only trace amounts of analyte material, as demonstrated in this study.

Crystalline sponge affinity screening: A fast tool for soaking condition optimization without the need of X-ray diffraction analysis.

Structural elucidation of small molecules only available in low quantity (nanogram) is one of the big advantages of the crystalline sponge method. The optimization of various soaking parameters is crucial for effective analyte absorption and repetitive positioning in the pores of the crystal. Time-consuming X-ray diffraction measurements are necessary for data collection and confirmation of successful guest inclusion. In this work, we report a screening method to select optimal soaking conditions without the need of single-crystal X-ray diffraction analysis for individual compounds and mixtures. 14 substances were chosen as test compounds. Parallel guest soaking of individual compounds and mixtures was conducted using various soaking conditions. After evaporation of solvent, excessive material was removed, and guest molecules released through dissolution of the framework. Liquid chromatography-tandem mass spectrometry allowed the estimation of analyte trapped in the pores and the selection of optimal soaking condition dependent on the highest amount of analyte to crystal size (affinity factor). The tool allowed subsequent crystallographic analysis of ten compounds with minimal experiment time. Additionally, a study to examine the lower limit of detection of the crystalline sponge method was conducted. Determination of two target analytes was possible using only 5 ng of sample. Our study shows the potential of an affinity screening to prioritize soaking parameters by estimation of the guest concentration in a single crystal for one or multiple target compounds within a short period of time.

The state of the field: from inception to commercialization of metal-organic frameworks.

As chemists and materials scientists, it is our duty to synthesize and utilize materials for a multitude of applications that promote the development of society and the well-being of its citizens. Since the inception of metal-organic frameworks (MOFs), researchers have proposed a variety of design strategies to rationally synthesize new MOF materials, studied their porosity and gas sorption performances, and integrated MOFs onto supports and into devices. Efforts have explored the relevance of MOFs for applications including, but not limited to, heterogeneous catalysis, guest delivery, water capture, destruction of nerve agents, gas storage, and separation. Recently, several start-up companies have undertaken MOF commercialization within industrial sectors. Herein, we provide a brief overview of the state of the MOF field from their design and synthesis to their potential applications, and finally, to their commercialization.

Crystalline Sponges as a Sensitive and Fast Method for Metabolite Identification: Application to Gemfibrozil and its Phase I and II Metabolites.

Understanding the metabolism of new drug candidates is important during drug discovery and development, as circulating metabolites may contribute to efficacy or cause safety issues. In the early phase of drug discovery, human in vitro systems are used to investigate human relevant metabolism. Though conventional techniques are limited in their ability to provide complete molecular structures of metabolites (liquid chromatography mass spectrometry) or require a larger amount of material not available from in vitro incubation (nuclear magnetic resonance), we here report for the first time the use of the crystalline sponge method to identify phase I and phase II metabolites generated from in vitro liver microsomes or S9 fractions. Gemfibrozil was used as a test compound. Metabolites generated from incubation with microsomes or S9 fractions, were fractionated using online fraction collection. After chromatographic purification and fractionation of the generated metabolites, single crystal X-ray diffraction of crystalline sponges was used to identify the structure of gemfibrozil metabolites. This technique allowed for complete structure elucidation of 5'-CH2OH gemfibrozil (M1), 4'-OH gemfibrozil (M2), 5'-COOH gemfibrozil (M3), and the acyl glucuronide of gemfibrozil, 1-O-ß-glucuronide (M4), the first acyl glucuronide available in the Cambridge Crystallographic Data Centre. Our study shows that when optimal soaking is possible, crystalline sponges technology is a sensitive (nanogram amount) and fast (few days) method that can be applied early in drug discovery to identify the structure of pure metabolites from in vitro incubations. SIGNIFICANCE STATEMENT: Complete structure elucidation of human metabolites plays a critical role in early drug discovery. Low amounts of material (nanogram) are only available at this stage and insufficient for nuclear magnetic resonance analysis. The crystalline sponge method has the potential to close this gap, as demonstrated in this study.

Computational Modeling to Quantify the Contributions of VEGFR1, VEGFR2, and Lateral Inhibition in Sprouting Angiogenesis.

Sprouting angiogenesis is a necessary process in regeneration and development as well as in tumorigenesis. VEGF-A is the main pro-angiogenic chemoattractant and it can bind to the decoy receptor VEGFR1 or to VEGFR2 to induce sprouting. Active sprout cells express Dll4, which binds to Notch1 on neighboring cells, in turn inhibiting VEGFR2 expression. It is known that the balance between VEGFR2 and VEGFR1 determines tip selection and network architecture, however the quantitative interrelationship of the receptors and their interrelated balances, also with relation to Dll4-Notch1 signaling, remains yet largely unknown. Here, we present an agent-based computer model of sprouting angiogenesis, integrating VEGFR1 and VEGFR2 in a detailed model of cellular signaling. Our model reproduces experimental data on VEGFR1 knockout. We show that soluble VEGFR1 improves the efficiency of angiogenesis by directing sprouts away from existing cells over a wide range of parameters. Our analysis unravels the relevance of the stability of the active notch intracellular domain as a dominating hub in this regulatory network. Our analysis quantitatively dissects the regulatory interactions in sprouting angiogenesis. Because we use a detailed model of intracellular signaling, the results of our analysis are directly linked to biological entities. We provide our computational model and simulation engine for integration in complementary modeling approaches.

The MYpop toolbox: Putting yeast stress responses in cellular context on single cell and population scales.

Systems biology holds the promise to integrate multiple sources of information in order to build ever more complete models of cellular function. To do this, the field must overcome two significant challenges. First, the current strategy to model average cells must be replaced with population based models accounting for cell-to-cell variability. Second, models must be integrated with each other and with basic cellular function. This requires a core model of cellular physiology as well as a multiscale simulation platform to support large-scale simulation of culture or tissues from single cells. Here, we present such a simulation platform with a core model of yeast physiology as scaffold to integrate and simulate SBML models. The software automates this integration helping users simulate their model of choice in context of the cell division cycle. We benchmark model merging, simulation and analysis by integrating a minimal model of osmotic stress into the core model and analyzing it. We characterize the effect of single cell differences on the dynamics of osmoadaptation, estimating when normal cell growth is resumed and obtaining an explanation for experimentally observed glycerol dynamics based on population dynamics. Hence, the platform can be used to reconcile single cell and population level data.

Dynamic metabolic models in context: biomass backtracking.

Mathematical modeling has proven to be a powerful tool to understand and predict functional and regulatory properties of metabolic processes. High accuracy dynamic modeling of individual pathways is thereby opposed by simplified but genome scale constraint based approaches. A method that links these two powerful techniques would greatly enhance predictive power but is so far lacking. We present biomass backtracking, a workflow that integrates the cellular context in existing dynamic metabolic models via stoichiometrically exact drain reactions based on a genome scale metabolic model. With comprehensive examples, for different species and environmental contexts, we show the importance and scope of applications and highlight the improvement compared to common boundary formulations in existing metabolic models. Our method allows for the contextualization of dynamic metabolic models based on all available information. We anticipate this to greatly increase their accuracy and predictive power for basic research and also for drug development and industrial applications.

Bud-Localization of CLB2 mRNA Can Constitute a Growth Rate Dependent Daughter Sizer.

Maintenance of cellular size is a fundamental systems level process that requires balancing of cell growth with proliferation. This is achieved via the cell division cycle, which is driven by the sequential accumulation and destruction of cyclins. The regulatory network around these cyclins, particularly in G1, has been interpreted as a size control network in budding yeast, and cell size as being decisive for the START transition. However, it is not clear why disruptions in the G1 network may lead to altered size rather than loss of size control, or why the S-G2-M duration also depends on nutrients. With a mathematical population model comprised of individually growing cells, we show that cyclin translation would suffice to explain the observed growth rate dependence of cell volume at START. Moreover, we assess the impact of the observed bud-localisation of the G2 cyclin CLB2 mRNA, and find that localised cyclin translation could provide an efficient mechanism for measuring the biosynthetic capacity in specific compartments: The mother in G1, and the growing bud in G2. Hence, iteration of the same principle can ensure that the mother cell is strong enough to grow a bud, and that the bud is strong enough for independent life. Cell sizes emerge in the model, which predicts that a single CDK-cyclin pair per growth phase suffices for size control in budding yeast, despite the necessity of the cell cycle network around the cyclins to integrate other cues. Size control seems to be exerted twice, where the G2/M control affects bud size through bud-localized translation of CLB2 mRNA, explaining the dependence of the S-G2-M duration on nutrients. Taken together, our findings suggest that cell size is an emergent rather than a regulatory property of the network linking growth and proliferation.

Dosage and Dose Schedule Screening of Drug Combinations in Agent-Based Models Reveals Hidden Synergies.

The fungus Candida albicans is the most common causative agent of human fungal infections and better drugs or drug combination strategies are urgently needed. Here, we present an agent-based model of the interplay of C. albicans with the host immune system and with the microflora of the host. We took into account the morphological change of C. albicans from the yeast to hyphae form and its dynamics during infection. The model allowed us to follow the dynamics of fungal growth and morphology, of the immune cells and of microflora in different perturbing situations. We specifically focused on the consequences of microflora reduction following antibiotic treatment. Using the agent-based model, different drug types have been tested for their effectiveness, namely drugs that inhibit cell division and drugs that constrain the yeast-to-hyphae transition. Applied individually, the division drug turned out to successfully decrease hyphae while the transition drug leads to a burst in hyphae after the end of the treatment. To evaluate the effect of different drug combinations, doses, and schedules, we introduced a measure for the return to a healthy state, the infection score. Using this measure, we found that the addition of a transition drug to a division drug treatment can improve the treatment reliability while minimizing treatment duration and drug dosage. In this work we present a theoretical study. Although our model has not been calibrated to quantitative experimental data, the technique of computationally identifying synergistic treatment combinations in an agent based model exemplifies the importance of computational techniques in translational research.

Assessing the advantage of morphological changes in Candida albicans: a game theoretical study.

A range of attributes determines the virulence of human pathogens. During interactions with their hosts, pathogenic microbes often undergo transitions between distinct stages, and the ability to switch between these can be directly related to the disease process. Understanding the mechanisms and dynamics of these transitions is a key factor in understanding and combating infectious diseases. The human fungal pathogen Candida albicans exhibits different morphotypes at different stages during the course of infection (candidiasis). For example, hyphae are considered to be the invasive form, which causes tissue damage, while yeast cells are predominant in the commensal stage. Here, we described interactions of C. albicans with its human host in a game theoretic model. In the game, players are fungal cells. Each fungal cell can adopt one of the two strategies: to exist as a yeast or hyphal cell. We characterized the ranges of model parameters in which the coexistence of both yeast and hyphal forms is plausible. Stability analysis of the system showed that, in theory, a reduced ability of the host to specifically recognize yeast and hyphal cells can result in bi-stability of the microbial populations' profile. Inspired by the model analysis we reasoned that the types of microbial interactions can change during invasive candidiasis. We found that positive cooperation among fungal cells occurs in mild infections and an enhanced tendency to invade the host is associated with negative cooperation. The model can easily be extended to multi-player systems with direct application to identifying individuals that enhance either positive or negative cooperation. Results of the modeling approach have potential application in developing treatment strategies.

Modeling yeast osmoadaptation at different levels of resolution.

We review the proposed mathematical models of the response to osmotic stress in yeast. These models mainly differ in the choice of mathematical representation (e.g. Bayesian networks, ordinary differential equations, or rule-based models), the extent to which the modeling is data-driven, and predictability. The overview exemplifies how one biological system can be modeled with various modeling techniques and at different levels of resolution, and how the choice typically is based on the amount and quality of available data, prior information of the system, and the research question in focus. As a natural part of the overview, we discuss requirements, advantages, and limitations of the different modeling approaches.

Zooming in on yeast osmoadaptation.

Saccharomyces cerevisiae is considered as a model organism for the investigation of cellular and molecular processes and gene regulation. Specifically, the response of S. cerevisiae to increase in osmolarity of the external medium (osmoadaptation) is a model adaptation process. The first mathematical model of volume changes in S. cerevisiae due to osmolarity has been proposed as early as 1983 by Schwartz and Diller (Cryobiology 20(5):542-552). Since then, both experimental and computational methods in biology have progressed dramatically. Especially in recent years, the study of response to hyperosmotic stress in S. cerevisiae by systems biology approaches has advanced rapidly. However, a holistic understanding of osmoadaptation combining environmental conditions, cellular preconditions, biophysical processes, molecular and biochemical network dynamics, has not yet been reached. Here, we review recent advances in the investigation of different aspects of osmoadaptation and discuss them with respect to an integrated view. This leads us to critically evaluate how to approach the goal of such an integrated view.

Formal representation of the high osmolarity glycerol pathway in yeast.

The high osmolarity glycerol (HOG) signalling system in yeast belongs to the class of Mitogen Activated Protein Kinase (MAPK) pathways that are found in all eukaryotic organisms. It includes at least three scaffold proteins that form complexes, and involves reactions that are strictly dependent on the set of species bound to a certain complex. The scaffold proteins lead to a combinatorial increase in the number of possible states. To date, representations of the HOG pathway have used simplifying assumptions to avoid this combinatorial problem. Such assumptions are hard to make and may obscure or remove essential properties of the system. This paper presents a detailed generic formal representation of the HOG system without such assumptions, showing the molecular interactions known from the literature. The model takes complexes into account, and summarises existing knowledge in an unambiguous and detailed representation. It can thus be used to anchor discussions about the HOG system. In the commonly used Systems Biology Markup Language (SBML), such a model would need to explicitly enumerate all state variables. The Kappa modelling language which we use supports representation of complexes without such enumeration. To conclude, we compare Kappa with a few other modelling languages and software tools that could also be used to represent and model the HOG system.

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

BACKGROUND: Gene 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. RESULTS: We 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. CONCLUSION: The 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.

Exploring the impact of osmoadaptation on glycolysis using time-varying response-coefficients.

We present a model of osmoadaptation in S. cerevisiae based on existing experimental and theoretical work. In order to investigate the impact of osmoadaptation on glycolysis, this model focuses on the interactions between glycolysis and osmoadaptation, namely the production of glycerol and its influence on flux towards pyruvate. Evaluation of this model shows that, depending on initial relations between glycerol and pyruvate production, the increased glycerol production can have a substantial negative effect on the pyruvate production rate. Existing experimental data and a detailed analysis of the model lead to the suggestion of an interaction between activated Hog1 and activators of glycolysis such as Pfk26.

Modeling development: spikes of the sea urchin.

Modeling of specification events during development poses new challenges to biochemical modeling. These include data limitations and a notorious absence of homeostasis in developing systems. The sea urchin is one of the best studied model organisms concerning development and a network, the Endomesoderm Network, has been proposed that is presumed to control endoderm and mesoderm specification in the embryo of Strongy-locentrotus purpuratus. We have constructed a dynamic model of a subnetwork of the Endomesoderm Network. In constructing the model, we had to resolve the following issues: choice of appropriate subsystem, assignment of embryonic data to cellular model, choice of appropriate kinetics. Although the resulting model is capable of reproducing fractions of the experimental data, it falls short of reproducing specification of cell types. These findings can facilitate the refinement of the Endomesoderm Network.

Coronary hybrid revascularization from January 1997 to January 2001: a clinical follow-up.

BACKGROUND: Hybrid revascularization (HyR), combining minimally invasive left internal mammary artery (LIMA) bypass grafting to the left anterior descending coronary artery (LAD) and catheter interventional treatment of the remaining coronary lesions, avoids the disadvantages associated with cardiopulmonary bypass (CPB). We investigated the clinical follow-up of 57 patients with multivessel disease undergoing this procedure in the last 4 years. METHODS: Between January 1997 and January 2001, 57 consecutive patients (41 men and 16 women, aged 65.7 +/- 7.9 years) with coronary artery disease (two-vessel, n = 34; three-vessel, n = 23) were treated with off-pump LIMA-to-LAD bypass combined with balloon angioplasty and stenting of the remaining significantly obstructed (> 50%) coronary vessels. Clinical follow-up data included a early postoperative and a 6-month control angiography and a patient interview in January 2001. RESULTS: All patients underwent LIMA-to-LAD bypass-grafting and balloon angioplasty in 72 coronary lesions without procedural-related complications. However, one early LIMA bypass occlusion was documented during coronary angiography. Postoperatively no deterioration of preexistent organ dysfunction was observed in any patient. The mean follow-up was 100.7 +/- 37.9 weeks in 55 of 57 patients (97%). Control angiography 6 months after HyR (n = 34) revealed a patent LIMA bypass in 33 patients and 8 in-stent restenoses (> 50%) in the coronary arteries that were treated interventionally by re-PTCA (n = 6) or by conventional CABG (n = 1). In 1 patient medical treatment resulted in significant reduction of angina so no further intervention was considered necessary. After HyR 1 patient died 18 months later of an intracerebral hemorrhage. All other patients are alive and doing well. CONCLUSIONS: Our results indicate that in selected patients with multivessel disease including left main stem stenosis HyR is an effective and secure procedure with excellent early and good midterm results. Especially elderly patients with severe concomitant diseases appear to benefit from this approach by avoiding CPB.