Dataset Information

The microbial selenoproteome of the Sargasso Sea.

ABSTRACT:

Background

Selenocysteine (Sec) is a rare amino acid which occurs in proteins in major domains of life. It is encoded by TGA, which also serves as the signal for termination of translation, precluding identification of selenoprotein genes by available annotation tools. Information on full sets of selenoproteins (selenoproteomes) is essential for understanding the biology of selenium. Herein, we characterized the selenoproteome of the largest microbial sequence dataset, the Sargasso Sea environmental genome project.

Results

We identified 310 selenoprotein genes that clustered into 25 families, including 101 new selenoprotein genes that belonged to 15 families. Most of these proteins were predicted redox proteins containing catalytic selenocysteines. Several bacterial selenoproteins previously thought to be restricted to eukaryotes were detected by analyzing eukaryotic and bacterial SECIS elements, suggesting that eukaryotic and bacterial selenoprotein sets partially overlapped. The Sargasso Sea microbial selenoproteome was rich in selenoproteins and its composition was different from that observed in the combined set of completely sequenced genomes, suggesting that these genomes do not accurately represent the microbial selenoproteome. Most detected selenoproteins occurred sporadically compared to the widespread presence of their cysteine homologs, suggesting that many selenoproteins recently evolved from cysteine-containing homologs.

Conclusions

This study yielded the largest selenoprotein dataset to date, doubled the number of prokaryotic selenoprotein families and provided insights into forces that drive selenocysteine evolution.

SUBMITTER: Zhang Y

PROVIDER: S-EPMC1088965 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BACKGROUND: Computational methods for identifying selenoproteins have been developed rapidly in recent years. However, it is still difficult to identify the open reading frame (ORF) of eukaryotic selenoprotein gene, because the TGA codon for a selenocysteine (Sec) residue in the active centre of selenoprotein is traditionally a terminal signal of protein translation. Although the identification of selenoproteins from genomes through bioinformatics methods has been conducted in bacteria, unicellular eukaryotes, insects and several vertebrates, only a few results have been reported on the ancient chordate selenoproteins. RESULTS: A gene assembly algorithm SelGenAmic has been constructed and presented in this study for identifying selenoprotein genes from eukaryotic genomes. A method based on this algorithm was developed to build an optimal TGA-containing-ORF for each TGA in a genome, followed by protein similarity analysis through conserved sequence alignments to screen out selenoprotein genes form these ORFs. This method improved the sensitivity of detecting selenoproteins from a genome due to the design that all TGAs in the genome were investigated for its possibility of decoding as a Sec residue. Using this method, eighteen selenoprotein genes were identified from the genome of Ciona intestinalis, leading to its member of selenoproteome up to 19. Among them a selenoprotein W gene was found to have two SECIS elements in the 3'-untranslated region. Additionally, the disulfide bond formation protein A (DsbA) was firstly identified as a selenoprotein in the ancient chordates of Ciona intestinalis, Ciona savignyi and Branchiostoma floridae, while selenoprotein DsbAs had only been found in bacteria and green algae before. CONCLUSION: The method based on SelGenAmic algorithm is capable of identifying eukaryotic selenoprotein genes from their genomes. Application of this method to Ciona intestinalis proves its successes in finding Sec-decoding TGA from large-scale eukaryotic genome sequences, which fills the gap in our knowledge on the ancient chordate selenoproteins.