Synthesis of a [6-pyridinyl-18F]-labelled fluoro derivative of WAY-100635 as a candidate radioligand for brain 5-HT1A receptor imaging with PET

In recent years, considerable effort has been spent on the design, synthesis and pharmacological characterization of radiofluorinated derivatives of the 5-HT(1A) receptor antagonist, WAY-100635, for the in vivo study of these receptors in human brain with PET. (Pyridinyl-6)-fluoro- and (pyridinyl-5)-fluoro-analogues of WAY-100635 (6-fluoro and 5-fluoro-WAY-100635, 5a/6a) were synthesized as well as the corresponding chloro-, bromo- and nitro-derivatives as precursors for labelling (5b-d and 6b-d). Comparative radiolabelling of these precursors with fluorine-18 (positron-emitting isotope, 109.8 min half-life) clearly demonstrated that only ortho-fluorination in this pyridine series, and not meta-fluorination, is of interest for the preparation of a radioligand by nucleophilic heteroaromatic substitution. 6-[(18)F]Fluoro-WAY-100635 ([(18)F]5a) can be efficiently synthesized in one step, either from the corresponding 6-bromo precursor (using conventional heating at 145 degrees C for 10 min) or from the corresponding 6-nitro precursor (using microwave activation at 100 W for 1 min). Typically, 15-25 mCi (0.55-0.92 GBq) of 6-[(18)F]fluoro-WAY-100635 ([(18)F]5a, 1-2 Ci/micromol or 37-72 GBq/micromol) were obtained in 50-70 min starting from a 100 mCi (3.7 GBq) aliquot of a batch of cyclotron-produced [(18)F]fluoride. This (18)F-labelled radioligand is now being evaluated in PET studies.     

Retention of antigen on follicular dendritic cells and B lymphocytes through complement-mediated multivalent ligand-receptor interactions: theory and application to HIV treatment

In HIV-infected patients, large quantities of HIV are associated with follicular dendritic cells (FDCs) in lymphoid tissue. During antiretroviral therapy, most of this virus disappears after six months of treatment, suggesting that FDC-associated virus has little influence on the eventual outcome of long-term therapy. However, a recent theoretical study using a stochastic model for the interaction of HIV with FDCs indicated that some virus may be retained on FDCs for years, where it can potentially reignite infection if treatment is interrupted. In that study, an approximate expression was used to estimate the time an individual virion remains on FDCs during therapy. Here, we determine the conditions under which this approximation is valid, and we develop expressions for the time a virion spends in any bound state and for the effect of rebinding on retention. We find that rebinding, which is influenced by diffusion, may play a major role in retention of HIV on FDCs. We also consider the possibility that HIV is retained on B cells during therapy, which like FDCs also interact with HIV. We find that virus associated with B cells is unlikely to persist during therapy.     

A mutational analysis reveals new functional interactions between domains of the Oxa1 protein in Saccharomyces cerevisiae

The Oxa1/YidC/Alb3 family plays a key role in the biogenesis of the respiratory and photosynthetic complexes in bacteria and organelles. In Saccharomyces cerevisiae, Oxa1 mediates the co‐translational insertion of mitochondrially encoded subunits of the three respiratory complexes III, IV and V within the inner membrane and also controls a late step in complex V assembly. No crystal structure of YidC or Oxa1 is available and little is known about the respective role of each transmembrane segment (TM) and hydrophilic loop of this polytopic protein on the biogenesis of the three complexes. Here, we have generated a collection of random point mutations located in the hydrophobic and hydrophilic domains of the protein and characterized their effects on the assembly of the three respiratory complexes. Our results show mutant‐dependent differential effects, particularly on complex V. In order to identify tertiary interactions within Oxa1, we have also isolated revertants carrying second‐site compensatory mutations able to restore respiration. This analysis reveals the existence of functional interactions between TM2 and TM5, TM4 and TM5 as well as between TM4 and loop 2, highlighting the key position of TM4 and TM5 in the Oxa1 protein.     

Hierarchical graphs for rule-based modeling of biochemical systems

Background  

In rule-based modeling, graphs are used to represent molecules: a colored vertex represents a component of a molecule, a vertex attribute represents the internal state of a component, and an edge represents a bond between components. Components of a molecule share the same color. Furthermore, graph-rewriting rules are used to represent molecular interactions. A rule that specifies addition (removal) of an edge represents a class of association (dissociation) reactions, and a rule that specifies a change of a vertex attribute represents a class of reactions that affect the internal state of a molecular component. A set of rules comprises an executable model that can be used to determine, through various means, the system-level dynamics of molecular interactions in a biochemical system.     

A network model of early events in epidermal growth factor receptor signaling that accounts for combinatorial complexity

We consider a model of early events in signaling by the epidermal growth factor (EGF) receptor (EGFR). The model includes EGF, EGFR, the adapter proteins Grb2 and Shc, and the guanine nucleotide exchange factor Sos, which is activated through EGF-induced formation of EGFR-Grb2-Sos and EGFR-Shc-Grb2-Sos assemblies at the plasma membrane. The protein interactions involved in signaling can potentially generate a diversity of protein complexes and phosphoforms; however, this diversity has been largely ignored in models of EGFR signaling. Here, we develop a model that accounts more fully for potential molecular diversity by specifying rules for protein interactions and then using these rules to generate a reaction network that includes all chemical species and reactions implied by the protein interactions. We obtain a model that predicts the dynamics of 356 molecular species, which are connected through 3749 unidirectional reactions. This network model is compared with a previously developed model that includes only 18 chemical species but incorporates the same scope of protein interactions. The predictions of this model are reproduced by the network model, which also yields new predictions. For example, the network model predicts distinct temporal patterns of autophosphorylation for different tyrosine residues of EGFR. A comparison of the two models suggests experiments that could lead to mechanistic insights about competition among adapter proteins for EGFR binding sites and the role of EGFR monomers in signal transduction.     

A computational model for early events in B cell antigen receptor signaling: analysis of the roles of Lyn and Fyn

BCR signaling regulates the activities and fates of B cells. BCR signaling encompasses two feedback loops emanating from Lyn and Fyn, which are Src family protein tyrosine kinases (SFKs). Positive feedback arises from SFK-mediated trans phosphorylation of BCR and receptor-bound Lyn and Fyn, which increases the kinase activities of Lyn and Fyn. Negative feedback arises from SFK-mediated cis phosphorylation of the transmembrane adapter protein PAG1, which recruits the cytosolic protein tyrosine kinase Csk to the plasma membrane, where it acts to decrease the kinase activities of Lyn and Fyn. To study the effects of the positive and negative feedback loops on the dynamical stability of BCR signaling and the relative contributions of Lyn and Fyn to BCR signaling, we consider in this study a rule-based model for early events in BCR signaling that encompasses membrane-proximal interactions of six proteins, as follows: BCR, Lyn, Fyn, Csk, PAG1, and Syk, a cytosolic protein tyrosine kinase that is activated as a result of SFK-mediated phosphorylation of BCR. The model is consistent with known effects of Lyn and Fyn deletions. We find that BCR signaling can generate a single pulse or oscillations of Syk activation depending on the strength of Ag signal and the relative levels of Lyn and Fyn. We also show that bistability can arise in Lyn- or Csk-deficient cells.     

Specification, annotation, visualization and simulation of a large rule-based model for ERBB receptor signaling

ABSTRACT: BACKGROUND: Mathematical/computational models are needed to understand cell signaling networks, which are complex. Signaling proteins contain multiple functional components and multiple sites of post-translational modification. The multiplicity of components and sites of modification ensures that interactions among signaling proteins have the potential to generate myriad protein complexes and post-translational modification states. As a result, the number of chemical species that can be populated in a cell signaling network, and hence the number of equations in an ordinary differential equation model required to capture the dynamics of these species, is prohibitively large. To overcome this problem, the rule-based modeling approach has been developed for representing interactions within signaling networks efficiently and compactly through coarse-graining of the chemical kinetics of molecular interactions. RESULTS: Here, we provide a demonstration that the rule-based modeling approach can be used to specify and simulate a large model for ERBB receptor signaling that accounts for site-specific details of protein-protein interactions. The model is considered large because it corresponds to a reaction network containing more reactions than can be practically enumerated. The model encompasses activation of ERK and Akt, and it can be simulated using a network-free simulator, such as NFsim, to generate time courses of phosphorylation for 55 individual serine, threonine, and tyrosine residues. The model is annotated and visualized in the form of an extended contact map. CONCLUSIONS: With the development of software that implements novel computational methods for calculating the dynamics of large-scale rule-based representations of cellular signaling networks, it is now possible to build and analyze models that include a significant fraction of the protein interactions that comprise a signaling network, with incorporation of the site-specific details of the interactions. Modeling at this level of detail is important for understanding cellular signaling.