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Evolutionary conservation of orthoretroviral long terminal repeats (LTRs) and ab initio detection of single LTRs in genomic data.

ABSTRACT: BACKGROUND: Retroviral LTRs, paired or single, influence the transcription of both retroviral and non-retroviral genomic sequences. Vertebrate genomes contain many thousand endogenous retroviruses (ERVs) and their LTRs. Single LTRs are difficult to detect from genomic sequences without recourse to repetitiveness or presence in a proviral structure. Understanding of LTR structure increases understanding of LTR function, and of functional genomics. Here we develop models of orthoretroviral LTRs useful for detection in genomes and for structural analysis. PRINCIPAL FINDINGS: Although mutated, ERV LTRs are more numerous and diverse than exogenous retroviral (XRV) LTRs. Hidden Markov models (HMMs), and alignments based on them, were created for HML- (human MMTV-like), general-beta-, gamma- and lentiretroviruslike LTRs, plus a general-vertebrate LTR model. Training sets were XRV LTRs and RepBase LTR consensuses. The HML HMM was most sensitive and detected 87% of the HML LTRs in human chromosome 19 at 96% specificity. By combining all HMMs with a low cutoff, for screening, 71% of all LTRs found by RepeatMasker in chromosome 19 were found. HMM consensus sequences had a conserved modular LTR structure. Target site duplications (TG-CA), TATA (occasionally absent), an AATAAA box and a T-rich region were prominent features. Most of the conservation was located in, or adjacent to, R and U5, with evidence for stem loops. Several of the long HML LTRs contained long ORFs inserted after the second A rich module. HMM consensus alignment allowed comparison of functional features like transcriptional start sites (sense and antisense) between XRVs and ERVs. CONCLUSION: The modular conserved and redundant orthoretroviral LTR structure with three A-rich regions is reminiscent of structurally relaxed Giardia promoters. The five HMMs provided a novel broad range, repeat-independent, ab initio LTR detection, with prospects for greater generalisation, and insight into LTR structure, which may aid development of LTR-targeted pharmaceuticals.

SUBMITTER: Benachenhou F

PROVIDER: S-EPMC2664473 | biostudies-literature | 2009

REPOSITORIES: biostudies-literature

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Evolutionary conservation of orthoretroviral long terminal repeats (LTRs) and ab initio detection of single LTRs in genomic data.

Benachenhou Farid F Jern Patric P Oja Merja M Sperber Göran G Blikstad Vidar V Somervuo Panu P Kaski Samuel S Blomberg Jonas J

PloS one 20090413 4

<h4>Background</h4>Retroviral LTRs, paired or single, influence the transcription of both retroviral and non-retroviral genomic sequences. Vertebrate genomes contain many thousand endogenous retroviruses (ERVs) and their LTRs. Single LTRs are difficult to detect from genomic sequences without recourse to repetitiveness or presence in a proviral structure. Understanding of LTR structure increases understanding of LTR function, and of functional genomics. Here we develop models of orthoretroviral ...[more]

PMID: 19365549

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Project description:BackgroundLong terminal repeats (LTRs, consisting of U3-R-U5 portions) are important elements of retroviruses and related retrotransposons. They are difficult to analyse due to their variability.The aim was to obtain a more comprehensive view of structure, diversity and phylogeny of LTRs than hitherto possible.ResultsHidden Markov models (HMM) were created for 11 clades of LTRs belonging to Retroviridae (class III retroviruses), animal Metaviridae (Gypsy/Ty3) elements and plant Pseudoviridae (Copia/Ty1) elements, complementing our work with Orthoretrovirus HMMs. The great variation in LTR length of plant Metaviridae and the few divergent animal Pseudoviridae prevented building HMMs from both of these groups.Animal Metaviridae LTRs had the same conserved motifs as retroviral LTRs, confirming that the two groups are closely related. The conserved motifs were the short inverted repeats (SIRs), integrase recognition signals (5´TGTTRNR…YNYAACA 3´); the polyadenylation signal or AATAAA motif; a GT-rich stretch downstream of the polyadenylation signal; and a less conserved AT-rich stretch corresponding to the core promoter element, the TATA box. Plant Pseudoviridae LTRs differed slightly in having a conserved TATA-box, TATATA, but no conserved polyadenylation signal, plus a much shorter R region.The sensitivity of the HMMs for detection in genomic sequences was around 50% for most models, at a relatively high specificity, suitable for genome screening.The HMMs yielded consensus sequences, which were aligned by creating an HMM model (a 'Superviterbi' alignment). This yielded a phylogenetic tree that was compared with a Pol-based tree. Both LTR and Pol trees supported monophyly of retroviruses. In both, Pseudoviridae was ancestral to all other LTR retrotransposons. However, the LTR trees showed the chromovirus portion of Metaviridae clustering together with Pseudoviridae, dividing Metaviridae into two portions with distinct phylogeny.ConclusionThe HMMs clearly demonstrated a unitary conserved structure of LTRs, supporting that they arose once during evolution. We attempted to follow the evolution of LTRs by tracing their functional foundations, that is, acquisition of RNAse H, a combined promoter/ polyadenylation site, integrase, hairpin priming and the primer binding site (PBS). Available information did not support a simple evolutionary chain of events.

Project description: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.

Dataset Information

Evolutionary conservation of orthoretroviral long terminal repeats (LTRs) and ab initio detection of single LTRs in genomic data.

Publications

Evolutionary conservation of orthoretroviral long terminal repeats (LTRs) and ab initio detection of single LTRs in genomic data.

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