Ca2+/calmodulin-binding peptides block phototransduction in Limulus ventral photoreceptors: Evidence for direct inhibition of phospholipase C

Phototransduction in Limulus photoreceptors involves a G protein-mediated activation of phospholipase C (PLC) and subsequent steps involving InsP3-mediated release of intracellular Ca2+. While exploring the role of calmodulin in this cascade, we found that intracellular injection of Ca2+/calmodulin-binding peptides (CCBPs) strongly inhibited the light response. By chemically exciting the cascade at various stages, we found the primary target of this effect was not in late stages of the cascade but rather at the level of G protein and PLC. That PLCδ1 contains a calmodulin-like structure raised the possibility that PLC might be directly affected by CCBPs. To test this possibility, in vitro experiments were conducted on purified PLC. The activity of this enzyme was strongly inhibited by CCBPs and also inhibited by calmodulin itself. Our results suggest that the calmodulin-like region of PLC has an important role in regulating this enzyme.

Wortmannin-Sensitive Phosphorylation, Translocation, and Activation of PLCγ1, but Not PLCγ2, in Antigen-stimulated RBL-2H3 Mast Cells

In RBL-2H3 tumor mast cells, cross-linking the high affinity IgE receptor (FcεRI) with antigen activates cytosolic tyrosine kinases and stimulates Ins(1,4,5)P3 production. Using immune complex phospholipase assays, we show that FcεRI cross-linking activates both PLCγ1 and PLCγ2. Activation is accompanied by the increased phosphorylation of both PLCγ isoforms on serine and tyrosine in antigen-treated cells. We also show that the two PLCγ isoforms have distinct subcellular localizations. PLCγ1 is primarily cytosolic in resting RBL-2H3 cells, with low levels of plasma membrane association. After antigen stimulation, PLCγ1 translocates to the plasma membrane where it associates preferentially with membrane ruffles. In contrast, PLCγ2 is concentrated in a perinuclear region near the Golgi and adjacent to the plasma membrane in resting cells and does not redistribute appreciably after FcεRI cross-linking. The activation of PLCγ1, but not of PLCγ2, is blocked by wortmannin, a PI 3-kinase inhibitor previously shown to block antigen-stimulated ruffling and to inhibit Ins(1,4,5)P3 synthesis. In addition, wortmannin strongly inhibits the antigen-stimulated phosphorylation of both serine and tyrosine residues on PLCγ1 with little inhibition of PLCγ2 phosphorylation. Wortmannin also blocks the antigen-stimulated translocation of PLCγ1 to the plasma membrane. Our results implicate PI 3-kinase in the phosphorylation, translocation, and activation of PLCγ1. Although less abundant than PLCγ2, activated PLCγ1 may be responsible for the bulk of antigen-stimulated Ins(1,4,5)P3 production in RBL-2H3 cells.

Structure-activity relationships derived by machine learning: the use of atoms and their bond connectivities to predict mutagenicity by inductive logic programming.

We present a general approach to forming structure-activity relationships (SARs). This approach is based on representing chemical structure by atoms and their bond connectivities in combination with the inductive logic programming (ILP) algorithm PROGOL. Existing SAR methods describe chemical structure by using attributes which are general properties of an object. It is not possible to map chemical structure directly to attribute-based descriptions, as such descriptions have no internal organization. A more natural and general way to describe chemical structure is to use a relational description, where the internal construction of the description maps that of the object described. Our atom and bond connectivities representation is a relational description. ILP algorithms can form SARs with relational descriptions. We have tested the relational approach by investigating the SARs of 230 aromatic and heteroaromatic nitro compounds. These compounds had been split previously into two subsets, 188 compounds that were amenable to regression and 42 that were not. For the 188 compounds, a SAR was found that was as accurate as the best statistical or neural network-generated SARs. The PROGOL SAR has the advantages that it did not need the use of any indicator variables handcrafted by an expert, and the generated rules were easily comprehensible. For the 42 compounds, PROGOL formed a SAR that was significantly (P < 0.025) more accurate than linear regression, quadratic regression, and back-propagation. This SAR is based on an automatically generated structural alert for mutagenicity.