Genome Biology and EvolutionGenomicsPeer-reviewed journal article
Extensive Loss and Gain of Conserved Non-Coding Elements during Early Teleost Evolution
Elisavet IliopoulouiD, Vasileios PapadogiannisiD, Costas S. TsigenopoulosiD, Tereza ManousakiiD
Abstract
Conserved Non-coding Elements (CNE) in vertebrates are enriched around transcription factor loci associated with development. However, loss and rapid divergence of CNEs has been reported in teleost fish, albeit taking only few genomes into consideration. Taking advantage of the recent increase in high-quality teleost genomes, we focus on studying the evolution of teleost CNEs, carrying out targeted genomic alignments and comparisons within the teleost phylogeny to detect CNEs and reconstruct the ancestral teleost CNE repertoire. This teleost-centric approach confirms previous observations of extensive vertebrate CNE loss early in teleost evolution, but also reveals massive CNE gain in the teleost stem-group over 300 million years ago. Using synteny-based association to link CNEs to their putatively regulated target genes, we show the most teleost gained CNEs are found in the vicinity of orthologous loci involved in transcriptional regulation and embryonic development that are also associated with CNEs in other vertebrates. Moreover, teleost and vertebrate CNEs share a highly similar motif and transcription factor binding site vocabulary. We suggest that early teleost CNE gains reflect a restructuring of the ancestral CNE repertoire through both extreme divergence and de novo emergence. Finally, we support newly identified pan-teleost CNEs have potential for accurate resolution of teleost phylogenetic placements in par with coding sequences, unlike ancestral only elements shared with spotted gar. This work provides new insight into CNE evolution with great value for follow-up work on phylogenomics, comparative genomics and the study of gene regulation evolution in teleosts.
- organism: multiple species
- assay: DNA methylation
- research focus: gene regulatory network inference
- approach: statistical model
- Citations
- 4
- FWCI
- 0.97
- Field percentile
- top 27%
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