Evidence for two uroporphyrinogen decarboxylase isoenzymes in human erythrocytes

In animals and plants, uroporphyrinogen decarboxylase catalyzes the stepwise decarboxylations of uroporphyrinogen, the precursor of heme and chlorophyll. To better understand its metabolic roles, we characterized the enzyme purified to electrophoretic homogeneity (about 11,000-fold) from human erythrocytes by a novel uroporphyrin-sepharose affinity chromatographic method. Native polyacrylamide disc gel electrophoresis of the purified enzyme preparation showed two bands detected by staining either for protein or with uroporphyrin-I. Each individual protein eluted from the gel when subjected to re-electrophoresis on SDS-polyacrylamide gel, appeared as a single protein band with molecular masses of approximately 54,000 and approximately 35,000 daltons respectively. Both proteins were able to catalyze all four decarboxylation steps, though the ratios of enzyme activity using octa-, hepta-, hexa- to pentacarboxylic porphyrinogen substrates were distinctly different. Also, their kinetic analysis with heptacarboxylic porphyrinogen-I substrate provided distinctly different apparent Michaelis constants. This provides the first evidence that decarboxylations of uroporphyrinogen to coproporphyrinogen are catalyzed by two isoenzymes.     

Doxepin, a tricyclic antidepressant, binds to normal, intact astroglial cells in cultures and inhibits the isoproterenol-induced increase in cyclic AMP production

Doxepin binds to intact astrocytes in primary cultures. The binding is competitively displaceable by excess cold doxepin and it is pronounced (Bmax = 27 nmol/mg protein) but the affinity is rather low (KD = 30 microM). The binding is inhibited by other antidepressants (amitriptyline, desipramine, tranylcypromine, iprindole) and propranolol but not by isoproterenol. Nevertheless, doxepin counteracts effectively the increase in the production of cyclic AMP evoked by isoproterenol.     

Field response of mycorrhizal and nonmycorrhizal Medicago sativa var. local in the F1 generation

Seeds were collected from plants of Medicago sativa var. local inoculated with Glomus macrocarpum and G. fasciculatum separately in pot experiments. These seeds were sown in garden soil and the percentage germination, general health and yield of subsequent plants (the F1 generation) were studied. The percentage germination was highest in seeds of G. macrocarpum-inoculated parents followed by those inoculated with G. fasciculatum; seeds of uninoculated parent plants showed the lowest germination. Vegetative yield of the progeny decreased in the order of plants inoculated with G. fasciculatum, with G. macrocarpum, and uninoculated. On the other hand, reproductive yield was highest for plants whose parents were inoculated with G. macrocarpum, followed by G. fascicullatum, and lowest for seeds of uninoculated parent plants.     

Direct Delivery of Inoculum to Shoot Tissue Interferes with Genotypic Resistance to Ralstonia solanacearum in Tomato Seedlings

Employing known susceptible and resistant genotypes and pure bacterial inoculum (0.1 OD; 10⁸ CFU/ml^?1), five different inoculation methods were tried to assess the response of tomato genotypes to Ralstonia solanacearum. This included seed‐soaking inoculation, seed‐sowing followed by inoculum drenching, or at 2‐week stage through petiole‐excision inoculation, soaking of planting medium with inoculum either directly or after imparting seedling root‐injury. Seed‐based inoculations or mere inoculum drenching at 2 weeks did not induce much disease in seedlings. Petiole inoculation induced 90–100% mortality in susceptible checks but also 50–60% mortality in normally resistant genotypes within 7–10 days. Root‐injury inoculation at 2‐week seedling stage appeared the best for early and clearer distinction between resistant and susceptible lines. The observations suggest a role played by the root system in governing genotypic resistance to the pathogen. Direct shoot inoculation is to be adopted only for selecting highly resistant lines or to thin down segregating populations during resistance breeding.     

Analysis of oligomeric proteins during unfolding by pH and temperature

During thermal transition and variation of pH, structural properties of 35 proteins and their complexes (bound with substrate and co-factor) were analyzed in detail. During pH alteration, these proteins were shown to have substantial differences in conformations. pH conformers were analyzed in detail. Free energy and other energy parameters were also estimated for these proteins at various pH and temperatures. Detailed structural analysis and binding interfaces of various substrates, inhibitors and cofactor of these proteins were also investigated using docking and molecular dynamic simulation. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

The development of sampling techniques for populations of the tarnished plant bug,Lygus lineolaris (hemiptera: Miridae)

Summary The relationship of ‘mean crowding’ to mean density showed that counts of nymphs and adults of the tarnished plant bug,Lygus lineolaris (Beauv.), on birdsfoot trefoil were aggregated and that the index of basic contagion approached zero for each developing stage. Aggregation increased gradually from the first to the fourth instar and then decreased through the adult stage. The mortality process from the first to the fifth instar was inversely density dependent. Inter-sample variance was the major source of population variance although significant variance was occasionally associated with blocks and plots owing to heterogeneity of the host stand. The most appropriate sample unit was a 3 sq. ft. area of foliage and substrate. This was divided into four quarters; application of the variance component technique indicated that in most cases one quarter drawn at random was optimal for sampling different stages. The number of samples required to attain a given level of precision varied inversely with population density. The cost function for sampling was determined for specified levels of precision.