Calmodulin (CaM), belonging to the tropinin C (TnC) superfamily, is one of the calcium-binding proteins that are highly conserved in their protein and gene structure. Based on the structure comparison among published vertebrate and invertebrate CaM, it is proposed that the ancestral form of eumetazoan CaM genes should have five exons and four introns (four-intron hypothesis). In this study, we determined the gene structure of CaM in the coral Acropora muricata, an anthozoan cnidarian representing the basal position in animal evolution. A CaM clone was isolated from a cDNA library constructed from the spawned eggs of A. muricata. This clone was composed of 908 nucleotides, including 162 base pairs (bp) of 5'-untranslated region (UTR), 296 bp of 3'-UTR, and an open reading frame 450 bp in length. The deduced amino acid indicated that the Acropora CaM protein is identical to that of the actiniarian, Metridinium senile, and has four putative calcium-binding domains highly similar to those of other vertebrate or invertebrate CaMs. Southern blot analysis revealed that Acropora CaM is a putative single-copy gene in the nuclear genome. Genomic sequencing showed that Acropora CaM was composed of five exons and four introns, with intron II not corresponding to any region in the actiniarian CaM gene, which possesses only four exons and three introns. Our results highlight that the coral CaM gene isolated from A. muricata has four introns at the predicted positions of the early metazoan CaM gene organization, providing the first evidence from the basal eumetazoan phylum to support the four-intron hypothesis.
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http://dx.doi.org/10.1007/s00239-008-9084-6 | DOI Listing |
Cancer Drug Resist
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Cancer Research Institute, Biomedical Research Center, Slovak Academy of Sciences, Bratislava 84505, Slovak Republic.
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January 2025
State Key Laboratory of Oncogenes and Related Genes, Shanghai Cancer Institute, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Department of Radiology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China. Electronic address:
Background: Accurate estimation of recurrence risk for cervical cancer plays a pivot role in making individualized treatment plans. We aimed to develop and externally validate an end-to-end deep learning model for predicting recurrence risk in cervical cancer patients following surgery by using multiparametric MRI images.
Methods: The clinicopathologic data and multiparametric MRI images of 406 cervical cancer patients from three institutions were collected.
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December 2024
Cancer Research UK Cambridge Institute, Li Ka Shing Centre, University of Cambridge, Cambridge CB2 0RE, United Kingdom
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College of Horticulture, Gansu Agricultural University, Lanzhou 730070, China.
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