Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Chromatin-based mechanisms to coordinate convergent overlapping transcription

Abstract

In eukaryotic genomes, the transcription units of genes often overlap with other protein-coding and/or noncoding transcription units1,2. In such intertwined genomes, the coordinated transcription of nearby or overlapping genes would be important to ensure the integrity of genome function3,4,5,6; however, the mechanisms underlying this coordination are largely unknown. Here, we show in Arabidopsis thaliana that genes with convergent orientation of transcription are major sources of antisense transcripts and that these genes transcribed on both strands are regulated by a putative Lysine-Specific Demethylase 1 family histone demethylase, FLOWERING LOCUS D (FLD)7,8. Our genome-wide chromatin profiling revealed that FLD, as well as its associating factor LUMINIDEPENDENS9, downregulates histone H3K4me1 in regions with convergent overlapping transcription. FLD localizes to actively transcribed genes, where it colocalizes with elongating RNA polymerase II phosphorylated at the Ser2 or Ser5 sites. Genome-wide transcription analyses suggest that FLD-mediated H3K4me1 removal negatively regulates the transcription of genes with high levels of antisense transcription. Furthermore, the effect of FLD on transcription dynamics is antagonized by DNA topoisomerase I. Our study reveals chromatin-based mechanisms to cope with overlapping transcription, which may occur by modulating DNA topology. This global mechanism to cope with overlapping transcription could be co-opted for specific epigenetic processes, such as cellular memory of responses to the environment10.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Fig. 1: FLD and LD decrease H3K4me1 around the TTSs of convergently transcribed genes.
Fig. 2: FLD localizes to the TTSs of actively transcribed genes.
Fig. 3: FLD controls the transcription of bidirectionally transcribed genes.
Fig. 4: FLD controls transcription dynamics around TTSs through DNA topoisomerase I function.

Data availability

The high-throughput sequencing data generated in this study are available in the NCBI, EBI and DDBJ databases under the accession number DRA010413.

Code availability

The scripts used for the analysis are available at GitHub: https://github.com/soinagak/FLD2021.

References

  1. 1.

    Mellor, J., Woloszczuk, R. & Howe, F. S. The interleaved genome. Trends Genet. 32, 57–71 (2016).

    CAS  PubMed  Google Scholar 

  2. 2.

    Kapranov, P., Willingham, A. T. & Gingeras, T. R. Genome-wide transcription and the implications for genomic organization. Nat. Rev. Genet. 8, 413–423 (2007).

    CAS  PubMed  Google Scholar 

  3. 3.

    Pelechano, V. & Steinmetz, L. M. Gene regulation by antisense transcription. Nat. Rev. Genet. 14, 880–893 (2013).

    CAS  PubMed  Google Scholar 

  4. 4.

    Leng, X., Thomas, Q., Rasmussen, S. H. & Marquardt, S. A G(enomic)P(ositioning)S(ystem) for plant RNAPII transcription. Trends Plant Sci. 25, 744–764 (2020).

    CAS  PubMed  Google Scholar 

  5. 5.

    Yu, X., Martin, P. G. P. & Michaels, S. D. BORDER proteins protect expression of neighboring genes by promoting 3′ Pol II pausing in plants. Nat. Commun. 10, 4359 (2012).

    Google Scholar 

  6. 6.

    Venkatesh, S., Li, H., Gogol, M. M. & Workman, J. L. Selective suppression of antisense transcription by Set2-mediated H3K36 methylation. Nat. Commun. 7, 13610 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  7. 7.

    He, Y., Michaels, S. D. & Amasino, R. M. Regulation of flowering time by histone acetylation in Arabidopsis. Science 302, 1751–1754 (2003).

    CAS  PubMed  Google Scholar 

  8. 8.

    Liu, F. et al. The Arabidopsis RNA-binding protein FCA requires a lysine-specific demethylase 1 homolog to downregulate FLC. Mol. Cell 28, 398–407 (2007).

    PubMed  Google Scholar 

  9. 9.

    Fang, X. et al. The 3′ processing of antisense RNAs physically links to chromatin-based transcriptional control. Proc. Natl Acad. Sci. USA 117, 15316–15321 (2020).

    PubMed  Google Scholar 

  10. 10.

    Ietswaart, R., Wu, Z. & Dean, C. Flowering time control: another window to the connection between antisense RNA and chromatin. Trends Genet. 28, 445–453 (2012).

    CAS  PubMed  Google Scholar 

  11. 11.

    Calo, E. & Wysocka, J. Modification of enhancer chromatin: what, how, and why? Mol. Cell 49, 825–837 (2013).

    CAS  PubMed  Google Scholar 

  12. 12.

    Inagaki, S. et al. Gene-body chromatin modification dynamics mediate epigenome differentiation in Arabidopsis. EMBO J. 36, 970–980 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  13. 13.

    Nielsen, M. et al. Transcription-driven chromatin repression of intragenic transcription start sites. PLoS Genet. 15, e1007969 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  14. 14.

    Shi, Y. et al. Histone demethylation mediated by the nuclear amine oxidase homolog LSD1. Cell 119, 941–953 (2004).

    CAS  PubMed  Google Scholar 

  15. 15.

    Harlen, K. M. & Churchman, L. S. The code and beyond: transcription regulation by the RNA polymerase II carboxy-terminal domain. Nat. Rev. Mol. Cell Biol. 18, 263–273 (2017).

    CAS  PubMed  Google Scholar 

  16. 16.

    Wang, Z., Wu, Z., Raitskin, O., Sun, Q. & Dean, C. Antisense-mediated FLC transcriptional repression requires the P-TEFb transcription elongation factor. Proc. Natl Acad. Sci. USA 111, 7468–7473 (2014).

    CAS  PubMed  Google Scholar 

  17. 17.

    Johanson, U. et al. Molecular analysis of FRIGIDA, a major determinant of natural variation in Arabidopsis flowering time. Science 290, 344–347 (2000).

    CAS  PubMed  Google Scholar 

  18. 18.

    Schmitz, R. J. & Amasino, R. M. Vernalization: a model for investigating epigenetics and eukaryotic gene regulation in plants. Biochim. Biophys. Acta 1769, 269–275 (2007).

    CAS  PubMed  Google Scholar 

  19. 19.

    Swiezewski, S., Liu, F., Magusin, A. & Dean, C. Cold-induced silencing by long antisense transcripts of an Arabidopsis Polycomb target. Nature 462, 799–802 (2009).

    CAS  PubMed  Google Scholar 

  20. 20.

    Kindgren, P., Ivanov, M. & Marquardt, S. Native elongation transcript sequencing reveals temperature dependent dynamics of nascent RNAPII transcription in Arabidopsis. Nucleic Acids Res. 48, 2332–2347 (2020).

    CAS  PubMed  Google Scholar 

  21. 21.

    Leng, X. et al. Organismal benefits of transcription speed control at gene boundaries. EMBO Rep. 21, e49315 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  22. 22.

    Liu, L. F. & Wang, J. C. Supercoiling of the DNA template during transcription. Proc. Natl Acad. Sci. USA 84, 7024–7027 (1987).

    CAS  PubMed  Google Scholar 

  23. 23.

    Kouzine, F., Levens, D. & Baranello, L. DNA topology and transcription. Nucleus 5, 195–202 (2014).

    PubMed  PubMed Central  Google Scholar 

  24. 24.

    Achar, Y. J., Adhil, M., Choudhary, R., Gilbert, N. & Foiani, M. Negative supercoil at gene boundaries modulates gene topology. Nature 577, 701–705 (2020).

    CAS  PubMed  Google Scholar 

  25. 25.

    Baranello, L. et al. RNA polymerase II regulates topoisomerase 1 activity to favor efficient transcription. Cell 165, 357–371 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  26. 26.

    Gong, X., Shen, L., Peng, Y. Z., Gan, Y. & Yu, H. DNA topoisomerase Iα affects the floral transition. Plant Physiol. 173, 642–654 (2016).

    PubMed  PubMed Central  Google Scholar 

  27. 27.

    Neil, H. et al. Widespread bidirectional promoters are the major source of cryptic transcripts in yeast. Nature 457, 1038–1042 (2009).

    CAS  PubMed  Google Scholar 

  28. 28.

    Xu, Z. et al. Bidirectional promoters generate pervasive transcription in yeast. Nature 457, 1033–1037 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  29. 29.

    Gullerova, M. & Proudfoot, N. J. Convergent transcription induces transcriptional gene silencing in fission yeast and mammalian cells. Nat. Struct. Mol. Biol. 19, 1193–1201 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  30. 30.

    Latos, P. A. et al. Airn transcriptional overlap, but not its lncRNA products, induces imprinted Igf2r silencing. Science 338, 1469–1472 (2012).

    CAS  PubMed  Google Scholar 

  31. 31.

    Pontier, D. B. & Gribnau, J. Xist regulation and function explored. Hum. Genet. 130, 223–236 (2011).

    PubMed  PubMed Central  Google Scholar 

  32. 32.

    Takahashi, T., Matsuhara, S., Abe, M. & Komeda, Y. Disruption of a DNA topoisomerase I gene affects morphogenesis in Arabidopsis. Plant Cell 14, 2085–2093 (2002).

    CAS  PubMed  PubMed Central  Google Scholar 

  33. 33.

    De Rybel, B. et al. A versatile set of ligation-independent cloning vectors for functional studies in plants. Plant Physiol. 156, 1292–1299 (2011).

    PubMed  PubMed Central  Google Scholar 

  34. 34.

    Hellens, R. P., Edwards, E. A., Leyland, N. R., Bean, S. & Mullineaux, P. M. pGreen: a versatile and flexible binary Ti vector for Agrobacterium-mediated plant transformation. Plant Mol. Biol. 42, 819–832 (2000).

    CAS  PubMed  Google Scholar 

  35. 35.

    Luo, C. & Lam, E. Quantitatively profiling genome-wide patterns of histone modifications in Arabidopsis thaliana using ChIP–seq. Methods Mol. Biol. 1112, 177–193 (2014).

    CAS  PubMed  Google Scholar 

  36. 36.

    Stasevich, T. J. et al. Regulation of RNA polymerase II activation by histone acetylation in single living cells. Nature 516, 272–275 (2014).

    CAS  PubMed  Google Scholar 

  37. 37.

    Gendrel, A. V., Lippman, Z., Martienssen, R. & Colot, V. Profiling histone modification patterns in plants using genomic tiling microarrays. Nat. Methods 2, 213–218 (2005).

    CAS  PubMed  Google Scholar 

  38. 38.

    Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  39. 39.

    Robinson, J. T. et al. Integrative genomics viewer. Nat. Biotechnol. 29, 24–26 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  40. 40.

    Quinlan, A. R. & Hall, I. M. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–842 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  41. 41.

    Shen, L., Shao, N., Liu, X. & Nestler, E. ngs.plot: quick mining and visualization of next-generation sequencing data by integrating genomic databases. BMC Genomics 15, 284 (2014).

    PubMed  PubMed Central  Google Scholar 

  42. 42.

    Zhang, Y. et al. Model-based analysis of ChIP–seq (MACS). Genome Biol. 9, R137 (2008).

    PubMed  PubMed Central  Google Scholar 

  43. 43.

    Ito, T. et al. Genome-wide negative feedback drives transgenerational DNA methylation dynamics in Arabidopsis. PLoS Genet. 11, e1005154 (2015).

    PubMed  PubMed Central  Google Scholar 

  44. 44.

    Luo, C. et al. Integrative analysis of chromatin states in Arabidopsis identified potential regulatory mechanisms for natural antisense transcript production. Plant J. 73, 77–90 (2012).

    PubMed  Google Scholar 

  45. 45.

    Stroud, H. et al. Genome-wide analysis of histone H3.1 and H3.3 variants in Arabidopsis thaliana. Proc. Natl Acad. Sci. USA 109, 5370–5375 (2012).

    CAS  PubMed  Google Scholar 

  46. 46.

    Wollmann, H. et al. The histone H3 variant H3.3 regulates gene body DNA methylation in Arabidopsis thaliana. Genome Biol. 18, 94 (2017).

    PubMed  PubMed Central  Google Scholar 

  47. 47.

    Zhang, F. et al. EIN2-dependent regulation of acetylation of histone H3K14 and non-canonical histone H3K23 in ethylene signalling. Nat. Commun. 7, 13018 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  48. 48.

    Veluchamy, A. et al. LHP1 regulates H3K27me3 spreading and shapes the three-dimensional conformation of the Arabidopsis genome. PLoS ONE 11, e0158936 (2016).

    PubMed  PubMed Central  Google Scholar 

  49. 49.

    Bewick, A. J. et al. On the origin and evolutionary consequences of gene body DNA methylation. Proc. Natl Acad. Sci. USA 113, 9111–9116 (2016).

    CAS  PubMed  Google Scholar 

  50. 50.

    Lu, L., Chen, X., Sanders, D., Qian, S. & Zhong, X. High-resolution mapping of H4K16 and H3K23 acetylation reveals conserved and unique distribution patterns in Arabidopsis and rice. Epigenetics 10, 1044–1053 (2015).

    PubMed  PubMed Central  Google Scholar 

  51. 51.

    Nassrallah, A. et al. DET1-mediated degradation of a SAGA-like deubiquitination module controls H2Bub homeostasis. eLife 7, e37892 (2018).

    PubMed  PubMed Central  Google Scholar 

  52. 52.

    Sing, T., Sander, O., Beerenwinkel, N. & Lengauer, T. ROCR: visualizing classifier performance in R. Bioinformatics 21, 3940–3941 (2005).

    CAS  PubMed  Google Scholar 

  53. 53.

    Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J. 17, 10–12 (2011).

    Google Scholar 

  54. 54.

    Zhu, J., Liu, M., Liu, X. & Dong, Z. RNA polymerase II activity revealed by GRO-seq and pNET-seq in Arabidopsis. Nat. Plants 4, 1112–1123 (2018).

    CAS  PubMed  Google Scholar 

Download references

Acknowledgements

We thank C. Dean and her colleagues for sharing their unpublished results on the physical association of FLD and LD and for comments on the manuscript. We also thank all Kakutani laboratory members for helpful discussion and technical assistance, T. Takahashi for the plant materials and S. Marquardt for the plaNET-seq data. This work used the Vincent J. Coates Genomics Sequencing Laboratory at UC Berkeley, supported by NIH S10 OD018174 Instrumentation Grant. The computations were partially performed on the NIG supercomputer at NIG, Japan. This work was supported by grants from Japan Science and Technology Agency (JST) PRESTO (no. JPMJPR17Q1) to S.I., and JST CREST (no. JPMJCR15O1) and Japan Society for the Promotion of Science (JSPS) (nos. 26221105, 15H05963 and 19H00995) to T.K.

Author information

Affiliations

Authors

Contributions

S.I. and T.K. designed the study. S.I., M.T. and K.T. performed the experiments. S.I. analysed the data. S.O. conducted the random forest analysis. S.I. and T.K. wrote the manuscript and incorporated comments from the other authors.

Corresponding author

Correspondence to Soichi Inagaki.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Plants thanks Sebastian Marquardt and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Effects of the fld mutation on H3K4me1 and H3K4me2.

a, Scatter plot comparing H3K4me2 (left) and H3 (right) levels between WT and fld. Each dot represents the RPM within each transcription unit (gene). b, Extended Data Fig. 1b, left, around FLC gene. c, Left: Venn diagrams showing reproducibility of H3K4me1 analysis in fld. In each replicate, regions with significantly increased and decreased H3K4me1 in fld compared to WT are identified using the macs2 program, and genes overlapping with these regions are identified and compared between replicate 1 and 2. The p-values is based on hypergeometric test for significant overrepresentation. Right: Scatter plot comparing H3K4me1 changes in fld compared to WT, between biological replicates. FLD target genes are colored in magenta. Pearson’s correlation coefficient is shown. d, Averaged profiles of H3K4me2 (left) and H3 (right) in WT and fld around “FLD targets” (c). The ribbon indicates 95% confidence interval for the mean. e,f, Patterns of H3K4me2, H3K4me1, and mRNA (top strand, magenta; bottom strand, dark blue) around pairs of convergent genes. Normalized coverage (per million mapped reads) is shown. Gene structures (box, exon; line, intron) in the top and bottom strands are shown.

Extended Data Fig. 2 Effects of the fld and ld mutations on H3K4me1.

a, Patterns of H3K4me1 and mRNA (top strand, magenta; bottom strand, dark blue) around the FLC gene. b, Box plots showing H3 and H3K4me1 levels in FLD target genes (Extended Data Fig. 1c) in two biological replicates. The p-values are based on two-sided Wilcoxson signed-rank test (n = 1,656 genes). Thick horizontal bar corresponds to the median, with the notch representing 95% confidence interval of the median. Upper and lower limits of box correspond to upper and lower quantile, respectively. Whisker indicates data range within 1.5× of the IQR and outliers are indicated by the dots. c, Heatmaps showing H3K4me1 changes between WT and mutants around genes sorted by the distance to downstream genes for convergent genes. The distance (plus value in blue, nonoverlapping; minus value in orange, overlapping) to the downstream gene for each gene is plotted in graph on left side.

Extended Data Fig. 3 Effects of convergent transcription on H3K4me1.

a, Patterns of H3K4me1 and mRNA in the At1g77120-At1g77122 pair in WT and T-DNA insertion line. The locations of T-DNA insertion is indicated by open triangle. b, Single base resolution coverage plot in T-DNA insertion line of the At1g77120-At1g77122 pair (biological replicates in left and middle) and in fld mutant (right) at the At1g77120-At1g77122 locus. At1g77120 locus is divided into 3 regions, and for each region the significances of differences of read coverages between WT and mutants are tested with Fisher’s exact test using uniquely mapped total reads, and p-values are shown on top of the graphs. CPM, Coverage Per Million mapped reads.

Extended Data Fig. 4 Localization pattern of the FLD protein.

a-c, Patterns of H3K4me1, mRNA (top strand, magenta; bottom strand, dark blue), and FLD localization determined by ChIP-seq of FLAG-FLD. FLAG ChIP-seq of nontransgenic WT Col-0 plants was used as a negative control. The shaded areas have increased H3K4me1 levels in fld compared to WT. d, Heatmaps showing FLAG-FLD enrichment around genes sorted by the distance to downstream genes for convergent and tandem genes separately. Genes are sorted and shown in the same way like in Fig. 1g.

Extended Data Fig. 5 Colocalization of the FLD protein and phospho-RNAPII.

a, Receiver-operator characteristic (ROC) curves showing FLC binding prediction accuracy of total RNAPII or phospho-RNAPII. A higher area under the ROC (AUC) value indicates higher accuracy as a predictor. b, c, Averaged profiles of RNAPII, RNAPII Ser5P, and RNAPII Ser2P and FLAG-FLD enrichment patterns around all convergent genes (b) and all tandem genes (c). The patterns of RNAPII Ser5P and Ser2P, with peaks at both ends of genes, are consistent with previously reported plant native elongating transcript sequencing (pNET-seq) that were conducted by using antibodies against RNAPII Ser5P and Ser2P, respectively54. d, e, RNAPII and FLAG-FLD profiles showing colocalization of FLAG-FLD and Ser2P and/or Ser5P of RNAPII.

Extended Data Fig. 6 chrRNA and mRNA in WT and fld.

a, Patterns of chrRNA and mRNA (top strand, magenta; bottom strand, dark blue) in WT and fld around the FLC locus. b, c, Patterns of H3K4me1, chrRNA, and mRNA around genes with highly increased H3K4me1 in fld compared to WT and with antisense chrRNA.

Extended Data Fig. 7 Antisense transcription, gene arrangement, and FLD-mediated H3K4me1 regulation.

a, Violin plots and boxplots of antisense plaNET-seq signal (data from ref. 21) for all genes (n = 32,427), FLD target genes (n = 1,656) and top FLD targets (n = 285). The p-values are based on two-sample Kolmogorov-Smirnov test. b, Violin plots and boxplots of antisense plaNET-seq signal in all convergent and tandem genes. The p-value is based on two-sided Mann-Whitney-Wilcoxon test comparing two groups of genes (n = 15,522 and 16,900 genes, respectively). c, Relationship between antisense transcript level of a gene (”gene-of-interest; GOI”) and sense transcript level of the downstream gene (DSG). All genes are divided into convergent and tandem genes, and then each group was further classified by antisense chrRNA level of GOI (red arrows in the bottom). Sense chrRNA levels of DSG (black arrows in the bottom) in all groups were shown by violin and box plots (n = 32,415, 15,520, 2,285, 195, 16,895, 811, 90 from left to right). The p-values are based on two-sample Kolmogorov-Smirnov test. d, Relationships between sense and antisense transcript levels. Sense chrRNA levels in WT are shown by violin and box plots for genes with indicated antisense chrRNA level (RPKM > 1 (n = 3,096) or >10 (n = 285)). The p-values are based on two-sample Kolmogorov-Smirnov test. e, Violin plots and boxplots of sense plaNET-seq signal for all genes (n = 32,427), FLD target gene (n = 1,656), and top FLD target genes (n = 285). The p-values are based on two-sample Kolmogorov-Smirnov test. f, Proportion of convergent and tandem genes in all nuclear genes (in TAIR10; 32,422), protein-coding genes that are overlapped with antisense transcripts (annotated as “antisense_long_noncoding_rna”, “long_noncoding_rna”, or “antisense_rna”) in Araport11 (1,321), and those in FLD target genes (241). Convergent genes are highly enriched in those two gene fractions (hypergeometric p = 2.36 x 10−171, and 6.39 x 10-58, respectively). g, h, Averaged profiles of H3K4me1 change between in WT and in fld (g), and antisense chrRNA (h), around antisense transcripts in Araport11 (1,321). The arrangement of protein-coding gene and antisense transcript pair is shown in the bottom. Two randomly selected populations (1,569 and 1,561, respectively) of size-matched regions within protein-coding genes are shown as controls. The ribbon indicates 95% confidence interval for the mean. i, Patterns of H3K4me1, chrRNA, and mRNA around an antisense transcript annotated in Araport11, At3g08215, which appears to be a part of read-through transcript from At3g51980.

Extended Data Fig. 8 Effects of the fld mutation on the RNAPII pattern.

a-c, Averaged profiles of RNAPII in WT and fld (left) and RNAPII changes (right) around genes categorized based on H3K4me1 increases in fld (top 1,656 genes (FLD targets), a; middle 1,656 genes, b; bottom 1,656 genes, c). The ribbon indicates the 95% confidence interval for the mean. d, Heatmaps showing RNAPII changes between WT and fld around transcription units sorted by H3K4me1 increases in fld. The top, middle, and bottom panels correspond to the top, middle, and bottom 1,656 genes, respectively. e-g, Averaged profiles of RNAPII in WT, fld, top1a, and fld top1a double mutant (top) and RNAPII changes in each mutant against WT (bottom). e, top 1,656 genes (FLD targets); f, middle 1,656 genes; g, bottom 1,656 genes.

Extended Data Fig. 9 Effect of fld and top1a mutations on flowering and transcription.

a, Representative picture of 1-month-old plants with indicated genotypes. Consistent with previous report (ref. 26), top1a mutation partially suppress the late flowering phenotype of the fld mutant. b, Sense mRNA level of FLC in WT and indicated mutants. RPM values in three biological replicates are plotted individually. c, Changes in sense mRNA level of Top FLD targets (n=180) between WT and top1a, and between fld and fld top1a. Thick horizontal bar corresponds to the median, with the notch representing 95% confidence interval of the median. Upper and lower limits of box correspond to upper and lower quantile, respectively. Whisker indicates data range within 1.5× of the IQR and outliers are indicated by the dots.

Extended Data Fig. 10 Model for the FLD-mediated regulation of convergent bidirectional transcription.

a, RNAPII transcribes along the gene body with H3K4me1 (blue circle with “M”), with the help of Top1 to resolve supercoils. H3K4me1 increases the rate of transcription elongation possibly by activating Top1. b, At the regions of convergent overlapping transcription, FLD and its cofactors decelerate transcription elongation through removal of H3K4me1. In this scenario, we speculate that the activities of FLD and Top1 antagonize each other, which was supported by our genetic analyses.

Supplementary information

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Inagaki, S., Takahashi, M., Takashima, K. et al. Chromatin-based mechanisms to coordinate convergent overlapping transcription. Nat. Plants 7, 295–302 (2021). https://doi.org/10.1038/s41477-021-00868-3

Download citation

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing