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Evolutionary analysis across mammals reveals distinct classes of long noncoding RNAs

ABSTRACT: Recent advances in transcriptome sequencing have enabled the discovery of thousands of long non-coding RNAs (lncRNAs) across many species. Though several lncRNAs have been shown to play important roles in diverse biological processes, the functions and mechanisms of most lncRNAs remain unknown. Two significant obstacles lie between transcriptome sequencing and functional characterization of lncRNAs: identifying truly non-coding genes from de novo reconstructed transcriptomes, and prioritizing the hundreds of resulting putative lncRNAs for downstream experimental interrogation. We present slncky, a computational lncRNA discovery tool that produces a high-quality set of lncRNAs from RNA-sequencing data and further uses evolutionary constraint to prioritize lncRNAs that are likely to be functionally important. Our automated filtering pipeline is comparable to manual curation efforts and more sensitive than previously published computational approaches. Furthermore, we develop a sensitive alignment pipeline for aligning lncRNA loci and propose new evolutionary metrics relevant for analyzing sequence and transcript evolution. Our analysis reveals that evolutionary selection acts in several distinct patterns, and uncovers two notable classes of intergenic lncRNAs: one showing strong purifying selection on RNA sequence and another where constraint is restricted to the regulation but not the sequence of the transcript.

ORGANISM(S): Mus musculus Rattus norvegicus Homo sapiens Mus musculus castaneus

PROVIDER: GSE64818 | GEO | 2016/01/07

SECONDARY ACCESSION(S): PRJNA271946

REPOSITORIES: GEO

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Project description:Recent advances in transcriptome sequencing have enabled the discovery of thousands of long non-coding RNAs (lncRNAs) across many species. Though several lncRNAs have been shown to play important roles in diverse biological processes, the functions and mechanisms of most lncRNAs remain unknown. Two significant obstacles lie between transcriptome sequencing and functional characterization of lncRNAs: identifying truly non-coding genes from de novo reconstructed transcriptomes, and prioritizing the hundreds of resulting putative lncRNAs for downstream experimental interrogation. We present slncky, a computational lncRNA discovery tool that produces a high-quality set of lncRNAs from RNA-sequencing data and further uses evolutionary constraint to prioritize lncRNAs that are likely to be functionally important. Our automated filtering pipeline is comparable to manual curation efforts and more sensitive than previously published computational approaches. Furthermore, we develop a sensitive alignment pipeline for aligning lncRNA loci and propose new evolutionary metrics relevant for analyzing sequence and transcript evolution. Our analysis reveals that evolutionary selection acts in several distinct patterns, and uncovers two notable classes of intergenic lncRNAs: one showing strong purifying selection on RNA sequence and another where constraint is restricted to the regulation but not the sequence of the transcript. To study a comprehensive and comparable set of lncRNAs expressed in the pluripotent state, we analyzed RNA-Seq data from pluripotent cells derived from several strains and species, and grown in two physiological conditions. First we derived ânaÃ¯veâ ES cells (ESCs) from each of three different mice strains: 129SvEv, NOD, and Mus musculus castaneus (cast) mouse, a wild mouse subspecies originally from Thailand, as well as naÃ¯ve induced pluripotent stem (iPS) cells from rat and human. Next, to facilitate comparisons across pluripotent cells from different species, we also cultured mouse and rat cells in âprimedâ epiblast stem cell (EpiSC) culture conditions, since human iPS cells in culture are much more similar molecularly and functionally to mouse primed EpiSCs rather than to a ground state naÃ¯ve ESCs. We polyA selected RNA from each cell type and deeply sequenced on HiSeq2500

Project description:Only a minuscule fraction of long non-coding RNAs (lncRNAs) are well characterized. The evolutionary history of lncRNAs can provide insights into their functionality, but comparative analyses have been precluded by our ignorance of lncRNAs in non-model organisms. Here, we use RNA sequencing to identify lncRNAs in eleven tetrapod species and we present the first large-scale evolutionary study of lncRNA repertoires and expression patterns. We identify ~11,000 primate- specific lncRNA families, which show evidence for selective constraint during recent evolution, and ~2,400 highly conserved lncRNAs (including ~400 genes that likely originated more than 300 million years ago). We find that lncRNAs, in particular ancient ones, are generally actively regulated and may predominantly function in embryonic development. lncRNA X-inactivation patterns reveal an extremely female-biased monotreme-specific lncRNA, which may partially compensate X-dosage in this lineage. Most lncRNAs evolve rapidly in terms of sequence and expression levels, but global patterns like tissue specificities are often conserved. We compared expression patterns of homologous lncRNA and protein-coding families across tetrapods to reconstruct an evolutionarily conserved co-expression network. This network, which surprisingly contains many lncRNA hubs, suggests potential functions for lncRNAs in fundamental processes like spermatogenesis or synaptic transmission, but also in more specific mechanisms such as placenta growth suppression through miRNA production. [Batch 1 and 2] To broaden our understanding of lncRNA evolution, we used an extensive RNA-seq dataset to establish lncRNA repertoires and homologous gene families in 11 tetrapod species. We analyzed the poly- adenylated transcriptomes of 8 organs (cortex/whole brain without cerebellum, cerebellum, heart, kidney, liver, placenta, ovary and testis) and 11 species (human, chimpanzee, bonobo, gorilla, orangutan, macaque, mouse, opossum, platypus, chicken and the frog Xenopus tropicalis), which shared a common ancestor ~370 millions of years (MY) ago. Our dataset included 47 strand-specific samples, which allowed us to confirm the orientation of gene predictions and to address the evolution of sense-antisense transcripts. See also GSE43721 (Soumillon et al, Cell Reports, 2013) for three strand-specific samples for mouse brain, liver and testis.

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Evolutionary analysis across mammals reveals distinct classes of long noncoding RNAs

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