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Prolactin (PRL) is one of the major hormones that control amphibian metamorphosis. Recently, a PRL (PRL1B) gene that is different from the known PRL (PRL1A) gene has been found in the genomes of several amphibian species. In order to ascertain whether the PRL1B gene is expressed in the bullfrog (Rana catesbeiana) pituitary, cloning of cDNA encoding PRL1B in the pituitary of the premetamorphic bullfrog tadpole was attempted. The bullfrog PRL1B amino acid sequence predicted from the obtained cDNA showed 62% identity with those of Xenopus PRL1Bs that have been presumed from the genome sequences, whereas the sequence identity between bullfrog PRL1A and PRL1B was 48%. A molecular phylogenetic tree showed that bullfrog PRL1B is most appropriately grouped with amphibian PRL1Bs. Quantitative PCR analysis revealed that the mRNA expression levels of bullfrog PRL1B in the pituitary were high during pre- and prometamorphosis, sharply declined at metamorphic climax and became undetectable after metamorphosis. In contrast, PRL1A mRNA levels were relatively low during pre- and prometamorphosis, rose at climax and remained high after metamorphosis. Immunohistochemical study using antibodies against partial peptides of PRL1A and PRL1B revealed that most of the PRL1A- and PRL1B-immunoreactive cells in the larval pituitary were distributed separately, but that some of the cells immunoreactive with both antibodies were also present. Western blot analysis with the larval pituitary extract indicated that PRL1B-immunoreactive band appeared at the position of molecular weight ca. 22.1 kDa and PRL1A-immunoreactive band at the position of ca. 22.8 kDa. The results obtained in this experiment suggest the possibility that PRL1B plays as-yet-unknown role(s) during the pre-climactic period of metamorphosis. This is the first report on the existence of PRL1B as a protein in the amphibian larval pituitary.
Pubmed ID: 30735673
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Web application for prediction of the presence and location of signal peptide cleavage sites in amino acid sequences from different organisms. The method incorporates a prediction of cleavage sites and a signal peptide/non-signal peptide prediction based on a combination of several artificial neural networks.
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