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Molecular Traits of Long Non-protein Coding RNAs from Diverse Plant Species Show Little Evidence of Phylogenetic Relationships.

ABSTRACT: Long non-coding RNAs (lncRNAs) represent a diverse class of regulatory loci with roles in development and stress responses throughout all kingdoms of life. LncRNAs, however, remain under-studied in plants compared to animal systems. To address this deficiency, we applied a machine learning prediction tool, Classifying RNA by Ensemble Machine learning Algorithm (CREMA), to analyze RNAseq data from 11 plant species chosen to represent a wide range of evolutionary histories. Transcript sequences of all expressed and/or annotated loci from plants grown in unstressed (control) conditions were assembled and input into CREMA for comparative analyses. On average, 6.4% of the plant transcripts were identified by CREMA as encoding lncRNAs. Gene annotation associated with the transcripts showed that up to 99% of all predicted lncRNAs for Solanum tuberosum and Amborella trichopoda were missing from their reference annotations whereas the reference annotation for the genetic model plant Arabidopsis thaliana contains 96% of all predicted lncRNAs for this species. Thus a reliance on reference annotations for use in lncRNA research in less well-studied plants can be impeded by the near absence of annotations associated with these regulatory transcripts. Moreover, our work using phylogenetic signal analyses suggests that molecular traits of plant lncRNAs display different evolutionary patterns than all other transcripts in plants and have molecular traits that do not follow a classic evolutionary pattern. Specifically, GC content was the only tested trait of lncRNAs with consistently significant and high phylogenetic signal, contrary to high signal in all tested molecular traits for the other transcripts in our tested plant species.

SUBMITTER: Simopoulos CMA

PROVIDER: S-EPMC6686929 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature

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Molecular Traits of Long Non-protein Coding RNAs from Diverse Plant Species Show Little Evidence of Phylogenetic Relationships.

Simopoulos Caitlin M A CMA Weretilnyk Elizabeth A EA Golding G Brian GB

G3 (Bethesda, Md.) 20190808 8

Long non-coding RNAs (lncRNAs) represent a diverse class of regulatory loci with roles in development and stress responses throughout all kingdoms of life. LncRNAs, however, remain under-studied in plants compared to animal systems. To address this deficiency, we applied a machine learning prediction tool, Classifying RNA by Ensemble Machine learning Algorithm (CREMA), to analyze RNAseq data from 11 plant species chosen to represent a wide range of evolutionary histories. Transcript sequences of ...[more]

PMID: 31235560

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Project description:BackgroundThe different hypotheses proposed to explain positive species richness-productivity relationships, i.e. selection effect and complementarity effect, imply that plant functional characteristics are at the core of a mechanistic understanding of biodiversity effects.Methodology/principal findingsWe used two community-wide measures of plant functional composition, (1) community-weighted means of trait values (CWM) and (2) functional trait diversity based on Rao's quadratic diversity (FD(Q)) to predict biomass production and measures of biodiversity effects in experimental grasslands (Jena Experiment) with different species richness (2, 4, 8, 16 and 60) and different functional group number and composition (1 to 4; legumes, grasses, small herbs, tall herbs) four years after establishment. Functional trait composition had a larger predictive power for community biomass and measures of biodiversitity effects (40-82% of explained variation) than species richness per se (<1-13% of explained variation). CWM explained a larger amount of variation in community biomass (80%) and net biodiversity effects (70%) than FD(Q) (36 and 38% of explained variation respectively). FD(Q) explained similar proportions of variation in complementarity effects (24%, positive relationship) and selection effects (28%, negative relationship) as CWM (27% of explained variation for both complementarity and selection effects), but for all response variables the combination of CWM and FD(Q) led to significant model improvement compared to a separate consideration of different components of functional trait composition. Effects of FD(Q) were mainly attributable to diversity in nutrient acquisition and life-history strategies. The large spectrum of traits contributing to positive effects of CWM on biomass production and net biodiversity effects indicated that effects of dominant species were associated with different trait combinations.Conclusions/significanceOur results suggest that the identification of relevant traits and the relative impacts of functional identity of dominant species and functional diversity are essential for a mechanistic understanding of the role of plant diversity for ecosystem processes such as aboveground biomass production.

Dataset Information

Molecular Traits of Long Non-protein Coding RNAs from Diverse Plant Species Show Little Evidence of Phylogenetic Relationships.

Publications

Molecular Traits of Long Non-protein Coding RNAs from Diverse Plant Species Show Little Evidence of Phylogenetic Relationships.

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