N-acetyl-sphingenine-1-phosphate is a potent calcium mobilizing agent.

Calcium mobilization induced by phosphorylated sphingoid bases was analyzed in calf pulmonary artery endothelial cells by confocal microscopy. A sphingenine-1-phosphate (SeP) analogue, N-acetyl-sphingenine-1-phosphate (N-C2-SeP), exogenously added to these cells, caused a fast and transient intracellular rise in calcium and was as potent as SeP. A minimal concentration of 0.6 nM for N-C2-SeP versus 1 nM for SeP was determined. The N-C2-SeP-induced Ca2+-signaling, like the response to SeP, was due to a release from thapsigargin-sensitive, ryanodine-insensitive, intracellular Ca2+-stores and not to a Ca2+-influx. N-C2-SeP can be considered as a truncated ceramide-phosphate, a lipid already reported to be mitogenic (Gomez-Munoz, A., Duffy, P.A., Martin, A., O'Brien, L., Byun, H.S., Bittman, R. and Brindley, D.N. (1995) Mol. Pharmacol. 47, 833-839), an effect that might be secondary to Ca2+-mobilization.     

Study on the enzymatic 5′-deiodination of 3′,5′-diiodothyronine using a radioimmunoassay for 3′-iodothyronine

A radioimmunoassay for 3′-iodothyronine has been developed. All iodothyronine analogues (except 3,3′-diiodothyronine) showed very little (0.02% at most) cross-reactivity, and the assay was sensitive to 1 pg 3′-iodothyronine/ tube. We have studied the 5′-deiodination of 3′,5′-diiodothyronine by rat liver microsomal fraction in the presence of dithiothreitol. Production of 3′-iodothyronine at 37°C was found to be linear with time of incubation up to 30 min and with concentration of microsomal protein up to 100 μg/ml. The reaction rate reached a limit on increasing 3′,5′-diiodothyronine concentration to 10 μM. The effect of pH on 3′-iodothyronine production was found to depend on 3′,5′-diiodothyronine concentration. Increasing 3′,5′-diiodothyronine concentration from 0.1 to 10 μM resulted in a shift of the pH optimum from 6–6.5 to 7.5. Similar effects on the 5′-deiodination of 3,3′,5′-triiodothyronine were observed, supporting the hypothesis that these reactions are catalysed by a single enzyme (iodothyronine 5′-deiodinase).     

Presymptomatic visualization of plant-virus interactions by thermography.

Salicylic acid (SA), produced by plants as a signal in defense against pathogens, induces metabolic heating mediated by alternative respiration in flowers of thermogenic plants, and, when exogenously applied, increases leaf temperature in nonthermogenic plants. We have postulated that the latter phenomenon would be detectable when SA is synthesized locally in plant leaves. Here, resistance to tobacco mosaic virus (TMV) was monitored thermographically before any disease symptoms became visible on tobacco leaves. Spots of elevated temperature that were confined to the place of infection increased in intensity from 8 h before the onset of visible cell death, and remained detectable as a halo around the ongoing necrosis. Salicylic acid accumulates during the prenecrotic phase in TMV-infected tobacco and is known to induce stomatal closure in certain species. We show that the time course of SA accumulation correlates with the evolution of both localized thermal effect and stomatal closure. Since the contribution of leaf respiration is marginal, we concluded that the thermal effect results predominantly from localized, SA-induced stomatal closure. The presymptomatic temperature increase could be of general significance in incompatible plant-pathogen interactions.     

Two-dimensional crystallization of bovine rhodopsin

Bovine rhodopsin has been clustered into two-dimensional crystals in highly purified native rod disk membranes and studied with negative staining and transmission electron microscopy. The lattice is P2(1) with dimensions of 8.3 X 7.9 nm and interaxis angles of 86 +/- 3 degrees. 110 images of ordered areas were digitized and aligned with computer-correlation methods to calculate an average image with diffraction to the fourth order. The images were computer-filtered and reconstructed to approx. 2 nm resolution. When crystals appeared they covered 20-40% of the surface of the preparation and, since rhodopsin is at least 95% of the protein, there is no doubt that the crystals were due to rhodopsin. There appear to be two rhodopsin dimers per unit cell. Each rhodopsin molecules takes up about 7.5 nm2 of membrane area and is estimated to be associated with about 12 lipids on each side of the membrane. The membrane area found for bovine rhodopsin supports the rhodopsin origin of rarely seen but more highly ordered two-dimensional crystals found in detergent-treated frog rod membranes (Corless, J.M., McCaslin, D.R. and Scott, B.L. (1982) Proc. Natl. Acad. Sci. USA 79, 1116-1120). Furthermore, the rhodopsin membrane area is close to that of bacteriorhodopsin and is consistent with a seven transmembrane helix structure proposed for rhodopsin (for references see Dratz, E.A. and Hargrave, D.A. (1983) Trends Biochem. Sci. 8, 128-131). Crystallization was accomplished by lowering the pH to 5.5 near the isoelectric point of rhodopsin, raising the salt concentration of 2 M (NH4)2SO4, adding 5% glucose and 0.02% Hibitane (Ayerst), a cationic amphipathic antiseptic that favored crystal growth.     

What life cycle graphs can tell about the evolution of life histories

We analyze long-term evolutionary dynamics in a large class of life history models. The model family is characterized by discrete-time population dynamics and a finite number of individual states such that the life cycle can be described in terms of a population projection matrix. We allow an arbitrary number of demographic parameters to be subject to density-dependent population regulation and two or more demographic parameters to be subject to evolutionary change. Our aim is to identify structural features of life cycles and modes of population regulation that correspond to specific evolutionary dynamics. Our derivations are based on a fitness proxy that is an algebraically simple function of loops within the life cycle. This allows us to phrase the results in terms of properties of such loops which are readily interpreted biologically. The following results could be obtained. First, we give sufficient conditions for the existence of optimisation principles in models with an arbitrary number of evolving traits. These models are then classified with respect to their appropriate optimisation principle. Second, under the assumption of just two evolving traits we identify structural features of the life cycle that determine whether equilibria of the monomorphic adaptive dynamics (evolutionarily singular points) correspond to fitness minima or maxima. Third, for one class of frequency-dependent models, where optimisation is not possible, we present sufficient conditions that allow classifying singular points in terms of the curvature of the trade-off curve. Throughout the article we illustrate the utility of our framework with a variety of examples.     

Effect of precursors on biosynthesis of monensins A and B

Precursors of monensins (acetate, propionate, butyrate, isobutyrate) affect the total production and the relative proportion of monensins A and B. Addition of propionate into the fermentation medium causes a prevalence of monensin B whereas butyrate and isobutyrate stimulate the production of monensin A and suppress the production of monensin B.     

The primary structure of histone H3 from the nematode Caenorhabditis elegans

The complete amino acid sequence of histone H3 (135 residues) from the nematode Caenorhabditis elegans has been established. Microheterogeneity occurs at positions 96 and 100 of the chain. The sequences of the nematode H3 isoforms are very similar to the major chain of calf thymus H3 with which they show 4 substitutions in total. The major variant has cysteine in position 96. This is the first report of cysteine in this position in H3 from non-mammalian tissue. An exceptional methylation site has been detected at position 79. Various other sites of secondary modification are of a conservative nature.     

Identifying essential genes in bacterial metabolic networks with machine learning methods

Background  

Identifying essential genes in bacteria supports to identify potential drug targets and an understanding of minimal requirements for a synthetic cell. However, experimentally assaying the essentiality of their coding genes is resource intensive and not feasible for all bacterial organisms, in particular if they are infective.