PubMed Health⌕ Search

Biomedical subjects

L A Ransnas

Publications and source records attributed to L A Ransnas.

4 recordsLinked to original sources

Immunoelectron microscopic identification of cytoplasmic and nuclear Gs alpha in S49 lymphoma cells.

The subcellular distribution of Gs alpha was characterized in S49 lymphoma cells with two polyclonal antisera directed against specific COOH- and NH2-terminal epitopes. Nonspecific binding was determined in each subcellular compartment by incubating cyc- S49 cells, known to be deficient in Gs alpha and its mRNA, with primary and secondary antisera. Small proportions of total specific binding sites were localized to the plasmalemma as well as the nuclear envelope. Because of their small size, these compartments contained a high concentration of Gs alpha. However, most of the specific binding sites were found in nonstructured cytoplasm and within the nucleus. Specific binding was abolished or significantly reduced by preincubating primary antisera with their peptide immunogens but not with an irrelevant peptide. Intracellular Gs alpha immunoreactive binding sites did not colocalize with gold-conjugated transferrin in cells preincubated with this ligand to mark a classical endocytotic pathway. The intracellular and intranuclear location of Gs alpha was confirmed with confocal microscopy of S49 cells immunostained with specific primary and fluorescently labeled secondary antibodies. Gs alpha was also detected with immunoblots of proteins extracted from purified S49 cell nuclei. Thus, Gs alpha is abundantly distributed in intracellular and intranuclear sites in S49 cells and occurs in loci distinct from organelles of the transferrin pathway. The substantial intracellular distribution of Gs alpha suggests that Gs may subserve intracellular and, perhaps, intranuclear functions that may be important in proliferating cells.

Animals↗

Differential amplification of antagonistic receptor pathways in neutrophils.

In human neutrophils approximately 500 ligand-occupied beta-adrenergic receptors almost completely inhibit the superoxide production generated by at least 50,000 formyl peptide receptors, suggesting a massive amplification of the inhibitory receptor signals. We estimated two stages of amplification. In the first stage, we quantitated the ligand-dependent GTPase activities. For the formyl peptide receptor, the number of phosphates released from GTP in the presence of the saturating ligand is relatively modest, i.e. approximately 1/min/receptor, even though there are approximately 200 Gn (Gi type II) proteins/formyl peptide receptor in neutrophil membranes. In contrast, the number of GTPs cleaved in the presence of a beta-adrenergic agonist is approximately 100/min/beta-adrenergic receptor, and there are about 700 Gs/beta-adrenergic receptor in membranes. Thus the signal of the beta-adrenergic receptor is already massively amplified at the G protein, whereas the signal of the formyl peptide receptor is likely to be amplified at subsequent steps. New kinetic evidence from intact cells and biochemical evidence from permeabilized cells is provided that the second messenger of the inhibitory pathway is cAMP. To estimate the amplification of this step, we determined the cAMP concentration necessary to maximally inhibit superoxide anion production of formyl peptide-stimulated electropermeabilized cells, and we compare these concentrations to previously determined values of cAMP production in neutrophils. We conclude that each receptor may generate up to 10,000 molecules of cAMP.

Cell Membrane↗

Noncoordinate regulation of cardiac Gs protein and beta-adrenergic receptors by a physiological stimulus, chronic dynamic exercise.

We used a physiological stimulus, chronic dynamic exercise, in pigs to examine resultant changes in chronotropic responsiveness to catecholamine and biochemical features of cardiac beta-adrenergic receptors and the stimulatory guanine nucleotide-binding protein, GS. Long-term treadmill running resulted in a substantial (44%) down-regulation of right atrial beta-adrenergic receptors, but the dose of isoproterenol yielding a 50% maximal increase in heart rate was decreased by 57% (from 0.07 +/- 0.03 to 0.03 +/- 0.01 microgram/kg; P less than 0.02) despite this decrease in receptor number. This disparity between receptor number and physiological responsiveness suggested altered signal transduction. We therefore quantitated GS in myocardial membranes obtained before and after chronic exercise in a competitive ELISA based on an antipeptide antibody developed to the alpha S portion of GS. We found a 42% increase in the amounts of GS in right atrial membranes (from 11.4 +/- 0.8 to 16.2 +/- 2.0 pmol/mg; P less than 0.05) and a 76% increase in the amounts of GS in left ventricular membranes (from 15.6 +/- 2.6 to 27.4 +/- 5.2 pmol/mg; P = 0.02) after chronic running. These data suggest that in the heart physiological perturbations can result in changes in the levels of GS, that GS and beta-adrenergic receptor number are not coordinately regulated, and that GS may contribute to altered adrenergic responsiveness independently of changes in beta-adrenergic receptor number.

Animals↗

Fitting curves to data using nonlinear regression: a practical and nonmathematical review.

Many types of data are best analyzed by fitting a curve using nonlinear regression, and computer programs that perform these calculations are readily available. Like every scientific technique, however, a nonlinear regression program can produce misleading results when used inappropriately. This article reviews the use of nonlinear regression in a practical and nonmathematical manner to answer the following questions: Why is nonlinear regression superior to linear regression of transformed data? How does nonlinear regression differ from polynomial regression and cubic spline? How do nonlinear regression programs work? What choices must an investigator make before performing nonlinear regression? What do the final results mean? How can two sets of data or two fits to one set of data be compared? What problems can cause the results to be wrong? This review is designed to demystify nonlinear regression so that both its power and its limitations will be appreciated.

Data Interpretation, Statistical↗