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Integrative classification of human coding and noncoding genes through RNA metabolism profiles

Nature Structural & Molecular Biology volume 24, pages 86–96 (2017)Cite this article

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Abstract

Pervasive transcription of the human genome results in a heterogeneous mix of coding RNAs and long noncoding RNAs (lncRNAs). Only a small fraction of lncRNAs have demonstrated regulatory functions, thus making functional lncRNAs difficult to distinguish from nonfunctional transcriptional byproducts. This difficulty has resulted in numerous competing human lncRNA classifications that are complicated by a steady increase in the number of annotated lncRNAs. To address these challenges, we quantitatively examined transcription, splicing, degradation, localization and translation for coding and noncoding human genes. We observed that annotated lncRNAs had lower synthesis and higher degradation rates than mRNAs and discovered mechanistic differences explaining slower lncRNA splicing. We grouped genes into classes with similar RNA metabolism profiles, containing both mRNAs and lncRNAs to varying extents. These classes exhibited distinct RNA metabolism, different evolutionary patterns and differential sensitivity to cellular RNA-regulatory pathways. Our classification provides an alternative to genomic context-driven annotations of lncRNAs.

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Figure 1: Progressive metabolic labeling of RNA.
Figure 2: Dynamics of intron excision.
Figure 3: RNA metabolism of mRNA and lncRNA.
Figure 4: Classification of genes according to RNA metabolism profiles.
Figure 5: Evolutionary and regulatory differences among RNA classes.
Figure 6: Distinct behavior of lncRNA classes.

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Acknowledgements

U.O. acknowledges support from an award from the US National Institutes of Health (R01-GM104962) and the Simons Institute for the Theory of Computing at UC Berkeley, where he was a long-term visitor in the Algorithmic Challenges in Genomics Program in the spring of 2015. N.M. acknowledges support from EU Marie Curie IIF.

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Authors and Affiliations

  1. Berlin Institute for Medical Systems Biology, Max Delbrück Center for Molecular Medicine, Berlin, Germany

    Neelanjan Mukherjee, Lorenzo Calviello, Antje Hirsekorn & Uwe Ohler

  2. Department of Biology, Humboldt University, Berlin, Germany

    Lorenzo Calviello & Uwe Ohler

  3. Center for Genomic Science of IIT@SEMM, Fondazione Instituto Italiano di Tecnologia, Milan, Italy

    Stefano de Pretis & Mattia Pelizzola

  4. Department of Computer Science, Humboldt University, Berlin, Germany

    Uwe Ohler

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  1. Neelanjan Mukherjee
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Contributions

N.M. and U.O. conceived the project; N.M. and U.O. developed the methodology; N.M., L.C. and S.d.P. developed software and performed formal analysis; N.M. and A.H. conducted the investigation; N.M. conducted the visualization; N.M. and U.O. wrote the original draft; L.C., S.d.P. and M.P. reviewed and edited the paper; N.M. and U.O. acquired funding; N.M. and U.O. provided resources; N.M. and U.O. supervised the project.

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Correspondence to Neelanjan Mukherjee or Uwe Ohler.

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Integrated supplementary information

Supplementary Figure 1 ERCC fit and table.

The fit between the number of expected ERCC molecules and the observed TPM measurement for total RNA depleted of rRNA with (a) ribozero or (b) RNAseH with oligos targetting rRNA, and (c) RNA that underwent one round of polyA selection. (d) Median gene expression (TPM) of 101 tissues/cell lines from strand-specific paired-end RNA seq generated by ENCODE. CPC distribution for the high expression population of (e) intronless and (f) multiexonic genes in HEK293 cells. (g) Boxplot of the distribution of primary RNA fraction for different data types for genes with mature RNA TPM > 1 in total RNA (n = 12,033 genes). Coverage depth compared to coverage breadth of 4SU and GROseq data for (h) the genome, (i) introns of coding genes, (j) intergenic enhancers, (k) exons of coding genes, (l) exons of lncRNA, and (m) introns of lncRNA.

Supplementary Figure 2 Splicing metrics, features and alternative models.

(a) Description of θ. (b) Features utilized in splicing models: physical features (orange), canonical splicing signals (blue) and splicing regulatory element density (blue). For details regarding calculation of splice site strengthp olypyrimidine tract score, branchpoint score and exonic splicing enhancer and silencer see Supplemental Experimental Procedures. (c) Violin plot of the θ calculated for introns of coding genes, lncRNA, mirtrons and snoRNA host introns. (d) The average r-squared for all regression models generated for each labeling time point and intron category. (e) The spearman correlation coefficient for each feature with θ for different feature categories and intron types. (f) The variable importance for different feature categories and intron types. The average NET-seq signal +/- 25 nucleotides from the 5' splice site for (g) total RNA polymerase II, (h) unphosphorylated RNA polymerase II, and (i) ser2p RNA polymerase II.

Supplementary Figure 3 Comparison of inferred rates.

Boxplot of the (a) synthesis, (b) processing, and (c) degradation rates. (d) The Pearson correlation between rates derived from all timepoints. (e) The distribution of polysomal vs cytosolic ratio for coding genes, lncRNAs, and pseudeogenes. (f) The distribution of synthesis rates for polyribosomal lncRNAs divided into groups based on the presence of a translated ORF.

Supplementary Figure 4 Characterizing class behavior.

(a) Optimal cluster number estimation by gap statistic. (b) Clustering GO enrichment for protein-coding genes and fold-enrichment of unclassified genes (grey). (c) Steady-state HEK293 expression distribution for each class. (d) Tissue specificity score distribution for each class and genes with inssuficient metabolic datain HEK293 cells. (e) Nuc/Cyt localization in mouse liver RNA-seq. (f) Odds-ratio for enrichment of the "core" and "missing " proteome.

Supplementary Figure 5 Regulatory and fitness differences.

a) Log 2 fold change of RBP perturbation - control for K562 ENCODE data. (b) Boxplot of cytoplasmic vs nuclear localization for genes grouped by origination class. A line is depicted connecting the means for each class (point).

Supplementary Figure 6 Characterization of lncRNAs in classes.

(a) The odds-ratio of the overlap between lncRNAs that were either found ("yes") or not found ("no") in lncRNADb for each lncRNA biotype. The numbers represent the gene count in each category. The fraction of nucleotides in a particular class with a fitCons score > S calculated for (b) coding exons and (c) 3' UTR exons of protein-coding genes and (d) all exons of lncRNA genes defined by GENCODE V19. The signal in the “sense-intronic” category, which are genes located in the intron of protein-coding genes, may be due to higher than background signal within introns of coding genes. (e-g) Coverage of 4SU (blue), total (green), cytoplasmic (red) and nuclear (cyan) RNA profiles for example lncRNAs from c6, c5, and c7, respectively. (h) Comparison of median values for each RNA metabolism feature by each class for coding genes (left) and lncRNAs (right).

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Mukherjee, N., Calviello, L., Hirsekorn, A. et al. Integrative classification of human coding and noncoding genes through RNA metabolism profiles. Nat Struct Mol Biol 24, 86–96 (2017). https://doi.org/10.1038/nsmb.3325

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