Different Characteristics of the Two Glutamate Synthases in the Green Leaves of Lycopersicon esculentum

The two glutamate synthases, NAD(P)H- and ferredoxin-dependent, from the green leaves of tomato plants (Lycopersicon esculentum L. cv Hellfrucht frühstamm) differed in their chemical properties and catalytic behavior. Gel filtration of NAD(P)H enzyme gave an apparent molecular size of 158 kilodalton, whereas the ferredoxin enzyme molecular size was 141 kilodalton. Arrhenius plots of the activities of the two enzymes showed that the NAD(P)H enzyme had two activation energies; 109.6 and 70.5 kilojoule per mole; the transition temperature was 22°C. The ferredoxin enzyme however, had only one activation energy; 56.1 kilojoule per mole. The respective catalytic activity pH optima for the NAD(P)H- dependent and the ferredoxin dependent enzymes were around 7.3 and 7.8. In experiments to evaluate the effects of modulators aspartate enhanced the NAD(P)H-linked activity, with a K_a value of 0.25 millimolar, but strongly inhibited that of the ferredoxin-dependent glutamate synthase with a K_i of 0.1 millimolar. 3-Phosphoserine was another inhibitor of the ferredoxin dependent enzyme with a K_i value of 4.9 millimolar. 3-Phosphoglyceric acid was a potent inhibitor of the ferredoxin-dependent form, but hardly affected the NAD(P)H-dependent enzyme. The results are discussed and interpreted to propose different specific functions that these activities may have within the leaf tissue cell.     

Identification of the spI products of Balbiani ring genes in Chironomus thummi

The spI fraction of high molecular weight secretory proteins was analysed in Chironomus thummi. These proteins are encoded by giant Balbiani ring (BR) genes which develop specifically in salivary gland cells. Each component of the spI fraction was studied electrophoretically from early and middle 4th instar larvae and prepupae, as well from galactose-treated larvae where changes in the relative puffing pattern of BR1 and BR2 are known to occur. The spI fraction consists of at least two bands with electrophoretic mobilities slower than those of the spI components of Camptochironomus. The slow migrating component remains throughout the 4th larval instar, while the amount of the faster component changes, being abundant in early 4th instar and prepupae, but not present (or very weak) in middle 4th instar. The correlated shifts in BR puffing pattern during these developmental stages suggest that the slow and fast components are encoded by BR2 and BR1. The spI fraction is modified by galactose treatment, the fast component being induced in parallel with a decrease in the slow component. These changes are correlated with changes in the steady-state levels of RNA: an increase in BR1 RNA and a decrease in BR2 RNA, and of proteins. These proteins could correspond to the spIb and spIa fractions allocated to BR2 and BR1, respectively, in Camptochironomus. After galactose treatment a new faster band sometimes appears, that could correspond to the spIc fraction of Camptochironomus. A possible spId equivalent was also identified. In conclusion the main features of the spI family in C. thummi are similar to those of spI in Camptochironomus.Abbreviations BR Balbiani ring - spI family of M_r=10⁶ secretory polypeptides     

Effect of nomegestrol acetate on estrone-sulfatase and 17β-hydroxysteroid dehydrogenase activities in human breast cancer cells

It is well recognized that estradiol (E₂) is one of the most important hormones supporting the growth and evolution of breast cancer. Consequently, to block this hormone before it enters the cancer cell or in the cell itself, has been one of the main targets in recent years. In the present study we explored the effect of the progestin, nomegestrol acetate, on the estrone sulfatase and 17β-hydroxy-steroid dehydrogenase (17β-HSD) activities of MCF-7 and T-47D human breast cancer cells. Using physiological doses of estrone sulfate (E₁S: 5 × 10⁻⁹ M), nomegestrol acetate blocked very significantly the conversion of E₁S to E₂. In the MCF-7 cells, using concentrations of 5 × 10⁻⁶ M and 5 × 10⁻⁵ M of nomegestrol acetate, the decrease of E₁S to E₂ was, respectively, −43% and −77%. The values were, respectively, −60% and −71% for the T-47D cells. Using E₁S at 2 × 10⁻⁶ M and nomegestrol acetate at 10⁻⁵ M, a direct inhibitory effect on the enzyme of −36% and −18% was obtained with the cell homogenate of the MCF-7 and T-47D cells, respectively. In another series of studies, it was observed that after 24 h incubation of a physiological concentration of estrone (E₁: 5 × 10⁻⁹ M) this estrogen is converted in a great proportion to E₂. Nomegestrol acetate inhibits this transformation by −35% and −85% at 5 × 10⁻⁷ M and 5 × 10⁻⁵ M, respectively in T-47D cells; whereas in the MCF-7 cells the inhibitory effect is only significant, −48%, at 5 × 10⁻⁵ M concentration of nomegestrol acetate. It is concluded that nomegestrol acetate in the hormone-dependent MCF-7 and T-47D breast cancer cells significantly inhibits the estrone sulfatase and 17β-HSD activities which converts E₁S to the biologically active estrogen estradiol. This inhibition provoked by this progestin on the enzymes involved in the biosynthesis of E₂ can open new clinical possibilities in breast cancer therapy.     

Comparative effects of galanin on isolated smooth muscle cells from ileum in five mammalian species.

Effect of galanin and CCK8 were studied on isolated smooth muscle cells obtained from pig, guinea-pig, rat, rabbit and dog ileum circular muscle layer. Galanin as well as CCK8 induced a concentration-dependent contraction of pig, rat, rabbit and guinea-pig ileum smooth muscle cells. Maximal contraction ranged between 23.7 +/- 1.9% and 26.1 +/- 3.1% decrease in cell length from control in the presence of both peptides. This maximal contraction was obtained at 1 nM galanin in pig, rat, rabbit, 1 nM CCK8 in rat, rabbit, guinea-pig, at 10 nM galanin in guinea-pig and 10 nM CCK8 in pig. Concentrations of galanin inducing a half maximal contraction (EC50) ranged between 8 pM and 80 pM in these species. In dog, CCK8 induced a concentration-dependent contraction of ileum smooth muscle cells, with a maximal contraction (24.5 +/- 2.3%) at 1nM and an EC50 of 50 pM while galanin inhibited cell contraction induced by CCK8. The CCK-induced contraction was abolished at 10 nM galanin and 10 nM VIP. Concentrations of galanin and VIP inducing a half-maximal relaxation of contracted cells were 2 pM and 3 pM respectively. It is concluded that galanin may induce cell contraction of pig, guinea-pig, rat and rabbit ileum circular muscle layer and cell relaxation of dog ileum by a direct myogenic effect.     

Convolutional neural networks improve species distribution modelling by capturing the spatial structure of the environment

Convolutional Neural Networks (CNNs) are statistical models suited for learning complex visual patterns. In the context of Species Distribution Models (SDM) and in line with predictions of landscape ecology and island biogeography, CNN could grasp how local landscape structure affects prediction of species occurrence in SDMs. The prediction can thus reflect the signatures of entangled ecological processes. Although previous machine-learning based SDMs can learn complex influences of environmental predictors, they cannot acknowledge the influence of environmental structure in local landscapes (hence denoted “punctual models”). In this study, we applied CNNs to a large dataset of plant occurrences in France (GBIF), on a large taxonomical scale, to predict ranked relative probability of species (by joint learning) to any geographical position. We examined the way local environmental landscapes improve prediction by performing alternative CNN models deprived of information on landscape heterogeneity and structure (“ablation experiments”). We found that the landscape structure around location crucially contributed to improve predictive performance of CNN-SDMs. CNN models can classify the predicted distributions of many species, as other joint modelling approaches, but they further prove efficient in identifying the influence of local environmental landscapes. CNN can then represent signatures of spatially structured environmental drivers. The prediction gain is noticeable for rare species, which open promising perspectives for biodiversity monitoring and conservation strategies. Therefore, the approach is of both theoretical and practical interest. We discuss the way to test hypotheses on the patterns learnt by CNN, which should be essential for further interpretation of the ecological processes at play.