Differentiation-Inducing Factor-1 and -2 Function also as Modulators for Dictyostelium Chemotaxis

BackgroundIn the early stages of development of the cellular slime mold Dictyostelium discoideum, chemotaxis toward cAMP plays a pivotal role in organizing discrete cells into a multicellular structure. In this process, a series of signaling molecules, such as G-protein-coupled cell surface receptors for cAMP, phosphatidylinositol metabolites, and cyclic nucleotides, function as the signal transducers for controlling dynamics of cytoskeleton. Differentiation-inducing factor-1 and -2 (DIF-1 and DIF-2) were originally identified as the factors (chlorinated alkylphenones) that induce Dictyostelium stalk cell differentiation, but it remained unknown whether the DIFs had any other physiologic functions.Methodology/Principal FindingsTo further elucidate the functions of DIFs, in the present study we investigated their effects on chemotaxis under various conditions. Quite interestingly, in shallow cAMP gradients, DIF-1 suppressed chemotaxis whereas DIF-2 promoted it greatly. Analyses with various mutants revealed that DIF-1 may inhibit chemotaxis, at least in part, via GbpB (a phosphodiesterase) and a decrease in the intracellular cGMP concentration ([cGMP]i). DIF-2, by contrast, may enhance chemotaxis, at least in part, via RegA (another phosphodiesterase) and an increase in [cGMP]i. Using null mutants for DimA and DimB, the transcription factors that are required for DIF-dependent prestalk differentiation, we also showed that the mechanisms for the modulation of chemotaxis by DIFs differ from those for the induction of cell differentiation by DIFs, at least in part.Conclusions/SignificanceOur findings indicate that DIF-1 and DIF-2 function as negative and positive modulators for Dictyostelium chemotaxis, respectively. To our knowledge, this is the first report in any organism of physiologic modulators (small molecules) for chemotaxis having differentiation-inducing activity

Distribution of the Octopamine Receptor AmOA1 in the Honey Bee Brain

Octopamine plays an important role in many behaviors in invertebrates. It acts via binding to G protein coupled receptors located on the plasma membrane of responsive cells. Several distinct subtypes of octopamine receptors have been found in invertebrates, yet little is known about the expression pattern of these different receptor subtypes and how each subtype may contribute to different behaviors. One honey bee (Apis mellifera) octopamine receptor, AmOA1, was recently cloned and characterized. Here we continue to characterize the AmOA1 receptor by investigating its distribution in the honey bee brain. We used two independent antibodies produced against two distinct peptides in the carboxyl-terminus to study the distribution of the AmOA1 receptor in the honey bee brain. We found that both anti-AmOA1 antibodies revealed labeling of cell body clusters throughout the brain and within the following brain neuropils: the antennal lobes; the calyces, pedunculus, vertical (alpha, gamma) and medial (beta) lobes of the mushroom body; the optic lobes; the subesophageal ganglion; and the central complex. Double immunofluorescence staining using anti-GABA and anti-AmOA1 receptor antibodies revealed that a population of inhibitory GABAergic local interneurons in the antennal lobes express the AmOA1 receptor in the cell bodies, axons and their endings in the glomeruli. In the mushroom bodies, AmOA1 receptors are expressed in a subpopulation of inhibitory GABAergic feedback neurons that ends in the visual (outer half of basal ring and collar regions) and olfactory (lip and inner basal ring region) calyx neuropils, as well as in the collar and lip zones of the vertical and medial lobes. The data suggest that one effect of octopamine via AmOA1 in the antennal lobe and mushroom body is to modulate inhibitory neurons

Method for evaluating thrombin concentration based on the data of monitoring the viscoelastic properties of native blood in the process of hemocoagulation

The process of native blood coagulation was studied with the resonant-acoustic method using the technology of low-frequency piezothromboelastography. It is shown that the experimental curve of time dependence of the piezoelectric sensor signal amplitude reflects the change in the rheological properties of blood during coagulation. A formula is obtained that relates the change in the concentration of thrombin to the rate of change in the complex coefficient of blood viscosity. A method has been developed for assessing thrombin concentration during fibrinogenesis based on the technology of piezothromboelastography using a resonant-acoustic method for determining the viscoelastic properties of whole blood. The results of calculating the thrombin concentration by this method are compared with the results of the thrombin generation test

“Artificial Intelligence Evoking Target Testing in Antidoping” (AR.I.E.T.T.A.)

Different substances and methods can be used to increase the oxygen carrying capacity of blood, thereby improving an athlete's ability to perform. Doping control procedures are expensive and the problem always exists of who we should test, by what criteria and when. Research groups have been developing criteria to detect these substances and methods (blood doping, human recombinant erythropoietin, oxygen carriers, the off/on model) International federations, including Biathlon, currently choose athletes based on random selection, standings, high hemoglobin and/or hematocrit and/or reticulocyte counts, off model scores, etc. There is currently no accurate integrated way to combine all variables (individual performance change and laboratory values), to estimate which athletes should be selected at the optimal time for anti-doping tests.This project aims to develop an intelligent system which is able to identify those athletes whose haematological and multiple variables reflect a pattern consistent with the use of banned substances or methods. These athletes could then be chosen at the optimal time for target testing. The focus of this project is the creation of a software program that will consider haematological values abnormal not only on the basis of high values, but also on the basis of raw data considered concurrently (haematological data in relation to the reference population, intraindividual haematological variations including abnormal low data, performance variations, ranking, nation). This system will produce classes of results associated to a diagnostic probability, useful for targeted selection for both in and out of competition controls. The system aims to be fast (analysing multiple data simultaneously), unpredictable and self-learning (the new informations will be automatically included to improve the knowledge). The project aims to provide a strong deterrent against doping, reducing the risk of evasion by manipulation, and to be cost-effective, ensuring that anti-doping budgets are spent in an evidence based fashion