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      Molecular evolutionary and structural analysis of the cytosolic DNA sensor cGAS and STING

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          Abstract

          Cyclic GMP-AMP (cGAMP) synthase (cGAS) is recently identified as a cytosolic DNA sensor and generates a non-canonical cGAMP that contains G(2′,5′)pA and A(3′,5′)pG phosphodiester linkages. cGAMP activates STING which triggers innate immune responses in mammals. However, the evolutionary functions and origins of cGAS and STING remain largely elusive. Here, we carried out comprehensive evolutionary analyses of the cGAS-STING pathway. Phylogenetic analysis of cGAS and STING families showed that their origins could be traced back to a choanoflagellate Monosiga brevicollis. Modern cGAS and STING may have acquired structural features, including zinc-ribbon domain and critical amino acid residues for DNA binding in cGAS as well as carboxy terminal tail domain for transducing signals in STING, only recently in vertebrates. In invertebrates, cGAS homologs may not act as DNA sensors. Both proteins cooperate extensively, have similar evolutionary characteristics, and thus may have co-evolved during metazoan evolution. cGAS homologs and a prokaryotic dinucleotide cyclase for canonical cGAMP share conserved secondary structures and catalytic residues. Therefore, non-mammalian cGAS may function as a nucleotidyltransferase and could produce cGAMP and other cyclic dinucleotides. Taken together, assembling signaling components of the cGAS-STING pathway onto the eukaryotic evolutionary map illuminates the functions and origins of this innate immune pathway.

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          Most cited references42

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          Estimating synonymous and nonsynonymous substitution rates under realistic evolutionary models.

          Q. Z. Yang (2000)
          Approximate methods for estimating the numbers of synonymous and nonsynonymous substitutions between two DNA sequences involve three steps: counting of synonymous and nonsynonymous sites in the two sequences, counting of synonymous and nonsynonymous differences between the two sequences, and correcting for multiple substitutions at the same site. We examine complexities involved in those steps and propose a new approximate method that takes into account two major features of DNA sequence evolution: transition/transversion rate bias and base/codon frequency bias. We compare the new method with maximum likelihood, as well as several other approximate methods, by examining infinitely long sequences, performing computer simulations, and analyzing a real data set. The results suggest that when there are transition/transversion rate biases and base/codon frequency biases, previously described approximate methods for estimating the nonsynonymous/synonymous rate ratio may involve serious biases, and the bias can be both positive and negative. The new method is, in general, superior to earlier approximate methods and may be useful for analyzing large data sets, although maximum likelihood appears to always be the method of choice.
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            Ab initio gene finding in Drosophila genomic DNA.

            Ab initio gene identification in the genomic sequence of Drosophila melanogaster was obtained using (human gene predictor) and Fgenesh programs that have organism-specific parameters for human, Drosophila, plants, yeast, and nematode. We did not use information about cDNA/EST in most predictions to model a real situation for finding new genes because information about complete cDNA is often absent or based on very small partial fragments. We investigated the accuracy of gene prediction on different levels and designed several schemes to predict an unambiguous set of genes (annotation CGG1), a set of reliable exons (annotation CGG2), and the most complete set of exons (annotation CGG3). For 49 genes, protein products of which have clear homologs in protein databases, predictions were recomputed by Fgenesh+ program. The first annotation serves as the optimal computational description of new sequence to be presented in a database. Reliable exons from the second annotation serve as good candidates for selecting the PCR primers for experimental work for gene structure verification. Our results shows that we can identify approximately 90% of coding nucleotides with 20% false positives. At the exon level we accurately predicted 65% of exons and 89% including overlapping exons with 49% false positives. Optimizing accuracy of prediction, we designed a gene identification scheme using Fgenesh, which provided sensitivity (Sn) = 98% and specificity (Sp) = 86% at the base level, Sn = 81% (97% including overlapping exons) and Sp = 58% at the exon level and Sn = 72% and Sp = 39% at the gene level (estimating sensitivity on std1 set and specificity on std3 set). In general, these results showed that computational gene prediction can be a reliable tool for annotating new genomic sequences, giving accurate information on 90% of coding sequences with 14% false positives. However, exact gene prediction (especially at the gene level) needs additional improvement using gene prediction algorithms. The program was also tested for predicting genes of human Chromosome 22 (the last variant of Fgenesh can analyze the whole chromosome sequence). This analysis has demonstrated that the 88% of manually annotated exons in Chromosome 22 were among the ab initio predicted exons. The suite of gene identification programs is available through the WWW server of Computational Genomics Group at http://genomic.sanger.ac.uk/gf. html.
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              Cyclic GMP-AMP containing mixed phosphodiester linkages is an endogenous high-affinity ligand for STING.

              The presence of microbial or self DNA in the cytoplasm of mammalian cells is a danger signal detected by the DNA sensor cyclic-GMP-AMP (cGAMP) synthase (cGAS), which catalyzes the production of cGAMP that in turn serves as a second messenger to activate innate immune responses. Here we show that endogenous cGAMP in mammalian cells contains two distinct phosphodiester linkages, one between 2'-OH of GMP and 5'-phosphate of AMP, and the other between 3'-OH of AMP and 5'-phosphate of GMP. This molecule, termed 2'3'-cGAMP, is unique in that it binds to the adaptor protein STING with a much greater affinity than cGAMP molecules containing other combinations of phosphodiester linkages. The crystal structure of STING bound to 2'3'-cGAMP revealed the structural basis of this high-affinity binding and a ligand-induced conformational change in STING that may underlie its activation. Copyright © 2013 Elsevier Inc. All rights reserved.
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                Author and article information

                Journal
                Nucleic Acids Res
                Nucleic Acids Res
                nar
                nar
                Nucleic Acids Research
                Oxford University Press
                0305-1048
                1362-4962
                01 September 2014
                30 June 2014
                30 June 2014
                : 42
                : 13
                : 8243-8257
                Affiliations
                [1 ]College of Life and Environmental Sciences, Hangzhou Normal University, Hangzhou 310036, China
                [2 ]Department of Biology, Duke University, Durham, NC 27708, USA
                [3 ]Department of Biological Sciences, University of North Texas, TX 76203, USA
                [4 ]Department of Immunology, Duke University, Durham, NC 27710, USA
                Author notes
                [* ]To whom correspondence should be addressed. Tel: +1 919 613 8152; Fax: +1 919 660 7293; Email: zpei@ 123456duke.edu
                Correspondence may also be addressed to Xiaomei Wu. Tel: +86 0571 28868542; Fax: +86 0571 28865333; Email: wuxm07@ 123456gmail.com
                Article
                10.1093/nar/gku569
                4117786
                24981511
                c0d26518-3af4-47e7-934d-ba997ddf0495
                © The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by-nc/3.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@ 123456oup.com

                History
                : 12 June 2014
                : 11 June 2014
                : 29 January 2014
                Page count
                Pages: 15
                Categories
                Computational Biology
                Custom metadata
                2014

                Genetics
                Genetics

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