A transaldolase : An enzyme implicated in crab steroidogenesis.

In arthropods, development is controlled by cholesterol-derived steroid hormones: the ecdysteroids. In vertebrates and insects, steroidogenesis is positively regulated and this is mediated by cAMP. In crustaceans, ecdysteroid biosynthesis by steroidogenic organs (Y-organs) is negatively regulated by a neuropeptide, the Molt Inhibiting Hormone (MIH). This neuropeptide-induced inhibition occurs via cyclic nucleotides and depends on protein synthesis. In the present work, we provide evidence that a major 36.2-kDa cytosolic protein (P36; pl: 6.8) from crab Y-organs is positively correlated with steroidogenic activity. On the basis of its amino acid sequence, P36 could be related to transaldolase, an enzyme of the pentose phosphate pathway which generates NADPH. In Y-organs, the enzymatic activity ofCarcinus transaldolase increases with steroidogenic activity, and MIH treatment decreases both synthesis and activity of transaldolase. Various transaldolases have been characterized in very distantly related groups, namely bacteria, yeasts, and humans. These enzymes are highly conserved and present strong structural homologies, interestingly the crab transaldolase is closest to that enzyme characterized in human cells.

Detection of compositional constraints in nucleic acid sequences using neural networks.

We describe in this paper a neural network method for the detection of compositional constraints in introns and exons. The first part of the algorithm (learning phase) consisted in presenting examples of intron and exon sequences to the network and in modifying its connections using the back-propagation algorithm. Previous connectionist methods achieved the learning of exons and introns using the latter as negative examples to the former. However, we chose to learn introns and exons jointly, using junk DNA as a common counter-example. In a second part (generalization phase), we tested the neural networks in the search for exons and introns in the human globin cluster. Their performances were also checked on the classification of unknown examples. As with the previous approaches, this technique discriminates introns and exons: values of the correlation coefficients are respectively 0.50 and 0.64 for the best achieved network. Moreover, using junk DNA sequences in the learning phase allows one to detect constrained regions inside the intron and the exon sequences (i.e. sequences that differ, by their nucleic acid compositions, from junk DNA). The application of our approach could be useful in the study of the internal organization of these sequences.