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Three-dimensional genome organization via triplex-forming RNAs

Nature Structural & Molecular Biology volume 28, pages 945–954 (2021)Cite this article

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Abstract

An increasing number of long noncoding RNAs (lncRNAs) have been proposed to act as nuclear organization factors during interphase. Direct RNA-DNA interactions can be achieved by the formation of triplex helix structures where a single-stranded RNA molecule hybridizes by complementarity into the major groove of double-stranded DNA. However, whether and how these direct RNA-DNA associations influence genome structure in interphase chromosomes remain poorly understood. Here we theorize that RNA organizes the genome in space via a triplex-forming mechanism. To test this theory, we apply a computational modeling approach of chromosomes that combines restraint-based modeling with polymer physics. Our models suggest that colocalization of triplex hotspots targeted by lncRNAs could contribute to large-scale chromosome compartmentalization cooperating, rather than competing, with architectural transcription factors such as CTCF.

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Fig. 1: Characterization of lncRNAs with triplex-forming potential.
Fig. 2: Characterization of co-TrTS hotspots in the genome.
Fig. 3: Co-TrTSs as drivers of chromosomal conformation.
Fig. 4: Cooperative effects of CTCF and ENST00000541775.1 on 3D genome structure.
Fig. 5: Compartment segregation mediated by co-TrTSs.

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Data availability

RNA-seq datasets were downloaded from ENCODE with accession nos. ENCSR000CPO, ENCSR000CQF, ENCSR000CQM, ENCSR530NHO and ENCSR000CPS. The PARS dataset was downloaded from Gene Expression Omnibus (GEO) with accession no. GSE50676. Hi-C was downloaded from GEO with accession no. GSE63525. CTCF ChIP–seq was downloaded from ENCODE with accession no. ENCSR000AKB. DNAse sequencing was downloaded from ENCODE with accession nos. ENCFF097LEF, ENCFF273MVV and ENCFF804BNU. The GENCODE v.19 lncRNA set was downloaded from https://www.gencodegenes.org/human/release_19.html.

Code availability

Custom scripts used to identify colocalizing loci and perform restraint-based modeling are available at http://sgt.cnag.cat/3dg/datasets/. The custom python scripts for analysis used the libraries Numpy107, Pandas108, Seaborn and Matplotlib109, Scipy110 and Multiprocessing111.

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Acknowledgements

We thank all current and past members of the Marti-Renom laboratory for their continuous discussions and support; H. Y. Chang, R. A. Flynn and K. Qu for help with PARS data analysis; J. Morf for fruitful discussions; M. Dabad and A. Esteve-Codina of the Functional Genomics Team at CNAG for initial RNA-seq analysis; and C.T. Wu and members of the Wu laboratory for their support. This work was supported by the European Research Council under the 7th Framework Program FP7/2007-2013 (ERC grant agreement no. 609989 to M.A.M.-R.) and the Spanish Ministerio de Ciencia, Innovación y Universidades through nos. IJCI-2015-23352 to I.F. and BFU2017-85926-P and PID2020-115696RB-I00 to M.A.M.-R. CRG acknowledges support from ‘Centro de Excelencia Severo Ochoa 2013-2017’, SEV-2012-0208 and the CERCA Program/Generalitat de Catalunya, as well as support from the Spanish Ministry of Science and Innovation through the Instituto de Salud Carlos III and the EMBL partnership, the Generalitat de Catalunya through Departament de Salut and Departament d’Empresa i Coneixement, and cofinancing with funds from the European Regional Development Fund by the Spanish Ministry of Science and Innovation corresponding to the Programa Opertaivo FEDER Plurirregional de España 2014–2020 and by the Secretaria d’Universitats i Recerca, Departament d’Empresa i Coneixement of the Generalitat de Catalunya corresponding to the program Operatiu FEDER Catalunya 2014–2020 and the NIH (to C.T. Wu no. R01HD091797 for supporting I.F.).

Author information

Author notes

  1. Marco Di Stefano

    Present address: Institute of Human Genetics, Centre National de la Recherche Scientifique, University of Montpellier, Montpellier, France

  2. Paula Soler-Vila

    Present address: Institut d’Investigacions Biomèdiques August Pi i Sunyer, Barcelona, Spain

Authors and Affiliations

  1. CNAG-CRG, Centre for Genomic Regulation, Barcelona Institute of Science and Technology, Barcelona, Spain

    Irene Farabella, Marco Di Stefano, Paula Soler-Vila, Maria Marti-Marimon & Marc A. Marti-Renom

  2. Gene Regulation, Stem Cells and Cancer Program, Centre for Genomic Regulation, Barcelona, Spain

    Marc A. Marti-Renom

  3. Universitat Pompeu Fabra, Barcelona, Spain

    Marc A. Marti-Renom

  4. ICREA, Barcelona, Spain

    Marc A. Marti-Renom

Authors
  1. Irene Farabella
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  2. Marco Di Stefano
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  3. Paula Soler-Vila
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  4. Maria Marti-Marimon
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  5. Marc A. Marti-Renom
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Contributions

I.F. and M.A.M.-R. conceived the study. I.F. and M.D.S. performed modeling. P.S.-V. and M.M.-M. supported modeling protocol development and implementation. I.F. wrote the manuscript with M.D.S., P.S.-V., M.M.-M. and M.A.M.-R. M.A.M.-R. oversaw the project.

Corresponding authors

Correspondence to Irene Farabella or Marc A. Marti-Renom.

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Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Structural & Molecular Biology thanks Sarah Harris and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Carolina Perdigoto and Beth Moorefield were the primary editors on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

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Extended data

Extended Data Fig. 1 Genomic features of co-localized Triplex Target hotspot.

(a) Percentage of TrTS that co-localize genome-wide versus the length of the identified lncRNAs with triplex forming potential. The blue line represents a linear regression model fit and the transparent shade is the 95% confidence interval. (b) Top three enriched motifs (Methods) for co-localizing Triplex Target Sites over background obtained with HOMER99. (c) Enrichment of co-TrTS hotspots in DNase I hypersensitive sites. (d) Number of lncRNAs with triplex forming potential with respect to chromosomal gene density. (e) Co-TrTS potential distribution per-chromosome for the four clusters defined in Fig. 2e. Box boundaries represent 1st and 3rd quartiles, middle line represents median, and whiskers extend to 1.5 times the interquartile range (two-sided Mann-Whitney rank test, using python default parameters, ***: p < 10−3; n = 897, 506, 667, and 575 for cluster 1, 2, 3, and 4, respectively. (f) Compartmentalization saddle plot (Methods) of all intra-chromosomal interactions in GM12878 cell line.

Extended Data Fig. 2 Restraint-based simulations for human chromosome 19.

(a,b) Contact maps derived from the ensemble of 3D models generated using co-localizing pairs of randomly selected loci (A) and low complexity enriched genomic sites (B). (C-F) Matrices of Pearson cross-correlation coefficients of top six eigenvectors for chromosome 19 of the experimental Hi-C compared to the four simulated datasets (that is, ENST00000541775.1, CTCF, random, and low complexity for c, d, e and f, respectively).

Extended Data Fig. 3 Comparisons of co-localized Triplex Target Sites restraint-based simulations.

(a) Distribution of the percentage of satisfied restraints in the ensemble of models using co-localizing pairs of loci driven by the ENST00000541775.1 co-TrTS hotspots and the CTCF enriched sites. Box boundaries represent 1st and 3rd quartiles, middle line represents median, and whiskers extend to 1.5 times the interquartile range; n = 1000 equal to the size of the 3D models ensemble. (b) Element-wise Spearman cross-correlation coefficients (spCCC) between the experimental Hi-C contact map and the contact maps derived from the 3D models generated using co-localizing pairs of loci driven by the co-TrTS hotspots of 7 representative lncRNAs with triplex potential belonging to cluster 4.

Extended Data Fig. 4 Correlation analysis of Hi-C and simulated contact maps for chromosome 22.

(a) Distribution of the diagonal cross-correlation coefficients (dCCC) (Methods) in chromosome 22 of the contact maps derived from the ensemble of 3D models with Hi-C. Box boundaries represent 1st and 3rd quartiles, middle line represents median, and whiskers extend to 1.5 times the interquartile range. The statistical significance of the difference between each pair of dCCC distributions has been assessed with the two-sided Mann-Whitney rank test using python default parameters. The p-values are < 10−3 unless reported; n = 1026 equal to the number of beads in chromosome 22.

Extended Data Fig. 5 Triplex forming lncRNAs govern long-range interactions.

(a-f) Diagonal correlations coefficient (dCCC) along the first 10 Mb between the experimental Hi-C contact map and the contact maps derived from the ensemble of the 3D models generated using lncRNAs with triplex potential from cluster 4: (A) ENST00000547963.1, (B) ENST0000043436.1, (C) ENST00000449111.1, (D) ENST00000561611.2, (E) ENST00000540866.2, and (F) ENST00000421202.1 co-TrTS hotspots for each of the 23 chromosomes (grey) and genome-wide average (blue). Vertical red bar marks 250 kb, which is the median length of convergent CTCF loops61. (g-i) Diagonal correlations coefficient (dCCC) along the first 10 Mb between the experimental Hi-C contact map and the contact maps derived from the ensemble of 3D models generated using CTCF enriched genomic loci (orange), ENST00000541775.1 co-TrTS hotspots (light blue), and CTCF & ENST00000541775.1 enriched genomic loci (yellow) for all (G) metacentric, (H) submetacentric, and (I) acrocentric chromosomes. Chromosomes are classified according to the standard Denver classification112.

Supplementary information

Reporting Summary

Supplementary Tables

Table 1. List of the 115 selected triplex-forming lncRNAs. Table 2. Percentage of significant Hi-C interactions used as restraints.

Supplementary Video 1

Randomly selected simulation from 1,000 trajectories in the ENST00000541775.1 co-TrTS hotspot ensemble. The simulated beads are colored according to their compartment type based on A/B calling derived from Hi-C data5 at 100-kb resolution (red and blue denote A- and B-type beads, respectively), showing segregation between compartment types in the simulation. The centromeric region is not shown.

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Farabella, I., Di Stefano, M., Soler-Vila, P. et al. Three-dimensional genome organization via triplex-forming RNAs. Nat Struct Mol Biol 28, 945–954 (2021). https://doi.org/10.1038/s41594-021-00678-3

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