Reciprocal regulation of PKA and rac signaling

Activated G protein-coupled receptors (GPCRs) and receptor tyrosine kinases relay extracellular signals through spatial and temporal controlled kinase and GTPase entities. These enzymes are coordinated by multifunctional scaffolding proteins for precise intracellular signal processing. The cAMP-dependent protein kinase A (PKA) is the prime example for compartmentalized signal transmission downstream of distinct GPCRs. A-kinase anchoring proteins tether PKA to specific intracellular sites to ensure precision and directionality of PKA phosphorylation events. Here, we show that the Rho-GTPase Rac contains A-kinase anchoring protein properties and forms a dynamic cellular protein complex with PKA. The formation of this transient core complex depends on binary interactions with PKA subunits, cAMP levels and cellular GTP-loading accounting for bidirectional consequences on PKA and Rac downstream signaling. We show that GTP-Rac stabilizes the inactive PKA holoenzyme. However, β-adrenergic receptor-mediated activation of GTP-Rac–bound PKA routes signals to the Raf-Mek-Erk cascade, which is critically implicated in cell proliferation. We describe a further mechanism of how cAMP enhances nuclear Erk1/2 signaling: It emanates from transphosphorylation of p21-activated kinases in their evolutionary conserved kinase-activation loop through GTP-Rac compartmentalized PKA activities. Sole transphosphorylation of p21-activated kinases is not sufficient to activate Erk1/2. It requires complex formation of both kinases with GTP-Rac1 to unleash cAMP-PKA–boosted activation of Raf-Mek-Erk. Consequently GTP-Rac functions as a dual kinase-tuning scaffold that favors the PKA holoenzyme and contributes to potentiate Erk1/2 signaling. Our findings offer additional mechanistic insights how β-adrenergic receptor-controlled PKA activities enhance GTP-Rac–mediated activation of nuclear Erk1/2 signaling

Ice nucleation by water-soluble macromolecules

Cloud glaciation is critically important for the global radiation budget (albedo) and for initiation of precipitation. But the freezing of pure water droplets requires cooling to temperatures as low as 235 K. Freezing at higher temperatures requires the presence of an ice nucleator, which serves as a template for arranging water molecules in an ice-like manner. It is often assumed that these ice nucleators have to be insoluble particles. We point out that also free macromolecules which are dissolved in water can efficiently induce ice nucleation: the size of such ice nucleating macromolecules (INMs) is in the range of nanometers, corresponding to the size of the critical ice embryo. As the latter is temperature-dependent, we see a correlation between the size of INMs and the ice nucleation temperature as predicted by classical nucleation theory. Different types of INMs have been found in a wide range of biological species and comprise a variety of chemical structures including proteins, saccharides, and lipids. Our investigation of the fungal species Acremonium implicatum, Isaria farinosa, and Mortierella alpina shows that their ice nucleation activity is caused by proteinaceous water-soluble INMs. We combine these new results and literature data on INMs from fungi, bacteria, and pollen with theoretical calculations to develop a chemical interpretation of ice nucleation and water-soluble INMs. This has atmospheric implications since many of these INMs can be released by fragmentation of the carrier cell and subsequently may be distributed independently. Up to now, this process has not been accounted for in atmospheric models

Ice nucleation by water-soluble macromolecules

Cloud glaciation is critically important for the global radiation budget (albedo) and for initiation of precipitation. But the freezing of pure water droplets requires cooling to temperatures as low as 235 K. Freezing at higher temperatures requires the presence of an ice nucleator, which serves as a template for arranging water molecules in an ice-like manner. It is often assumed that these ice nucleators have to be insoluble particles. We point out that also free macromolecules which are dissolved in water can efficiently induce ice nucleation: the size of such ice nucleating macromolecules (INMs) is in the range of nanometers, corresponding to the size of the critical ice embryo. As the latter is temperature-dependent, we see a correlation between the size of INMs and the ice nucleation temperature as predicted by classical nucleation theory. Different types of INMs have been found in a wide range of biological species and comprise a variety of chemical structures including proteins, saccharides, and lipids. Our investigation of the fungal species Acremonium implicatum, Isaria farinosa, and Mortierella alpina shows that their ice nucleation activity is caused by proteinaceous water-soluble INMs. We combine these new results and literature data on INMs from fungi, bacteria, and pollen with theoretical calculations to develop a chemical interpretation of ice nucleation and water-soluble INMs. This has atmospheric implications since many of these INMs can be released by fragmentation of the carrier cell and subsequently may be distributed independently. Up to now, this process has not been accounted for in atmospheric models

Identification of Novel Functional Inhibitors of Acid Sphingomyelinase

We describe a hitherto unknown feature for 27 small drug-like molecules, namely functional inhibition of acid sphingomyelinase (ASM). These entities named FIASMAs (Functional Inhibitors of Acid SphingoMyelinAse), therefore, can be potentially used to treat diseases associated with enhanced activity of ASM, such as Alzheimer's disease, major depression, radiation- and chemotherapy-induced apoptosis and endotoxic shock syndrome. Residual activity of ASM measured in the presence of 10 µM drug concentration shows a bimodal distribution; thus the tested drugs can be classified into two groups with lower and higher inhibitory activity. All FIASMAs share distinct physicochemical properties in showing lipophilic and weakly basic properties. Hierarchical clustering of Tanimoto coefficients revealed that FIASMAs occur among drugs of various chemical scaffolds. Moreover, FIASMAs more frequently violate Lipinski's Rule-of-Five than compounds without effect on ASM. Inhibition of ASM appears to be associated with good permeability across the blood-brain barrier. In the present investigation, we developed a novel structure-property-activity relationship by using a random forest-based binary classification learner. Virtual screening revealed that only six out of 768 (0.78%) compounds of natural products functionally inhibit ASM, whereas this inhibitory activity occurs in 135 out of 2028 (6.66%) drugs licensed for medical use in humans

Qualitative prediction of blood–brain barrier permeability on a large and refined dataset

The prediction of blood–brain barrier permeation is vitally important for the optimization of drugs targeting the central nervous system as well as for avoiding side effects of peripheral drugs. Following a previously proposed model on blood–brain barrier penetration, we calculated the cross-sectional area perpendicular to the amphiphilic axis. We obtained a high correlation between calculated and experimental cross-sectional area (r = 0.898, n = 32). Based on these results, we examined a correlation of the calculated cross-sectional area with blood–brain barrier penetration given by logBB values. We combined various literature data sets to form a large-scale logBB dataset with 362 experimental logBB values. Quantitative models were calculated using bootstrap validated multiple linear regression. Qualitative models were built by a bootstrapped random forest algorithm. Both methods found similar descriptors such as polar surface area, pKa, logP, charges and number of positive ionisable groups to be predictive for logBB. In contrast to our initial assumption, we were not able to obtain models with the cross-sectional area chosen as relevant parameter for both approaches. Comparing those two different techniques, qualitative random forest models are better suited for blood-brain barrier permeability prediction, especially when reducing the number of descriptors and using a large dataset. A random forest prediction system (ntrees = 5) based on only four descriptors yields a validated accuracy of 88%