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.

Transcriptome-wide dynamics of extensive m6A mRNA methylation during Plasmodium falciparum blood-stage development

Abstract

Malaria pathogenesis results from the asexual replication of Plasmodium falciparum within human red blood cells, which relies on a precisely timed cascade of gene expression over a 48-h life cycle. Although substantial post-transcriptional regulation of this hardwired program has been observed, it remains unclear how these processes are mediated on a transcriptome-wide level. To this end, we identified mRNA modifications in the P. falciparum transcriptome and performed a comprehensive characterization of N6-methyladenosine (m6A) over the course of blood-stage development. Using mass spectrometry and m6A RNA sequencing, we demonstrate that m6A is highly developmentally regulated, exceeding m6A levels known in any other eukaryote. We characterize a distinct m6A writer complex and show that knockdown of the putative m6A methyltransferase, PfMT-A70, by CRISPR interference leads to increased levels of transcripts that normally contain m6A. In accordance, we find an inverse correlation between m6A methylation and mRNA stability or translational efficiency. We further identify two putative m6A-binding YTH proteins that are likely to be involved in the regulation of these processes across the parasite’s life cycle. Our data demonstrate unique features of an extensive m6A mRNA methylation programme in malaria parasites and reveal its crucial role in dynamically fine-tuning the transcriptional cascade of a unicellular eukaryote.

This is a preview of subscription content, access via your institution

Relevant articles

Open Access articles citing this article.

Access options

Buy article

Get time limited or full article access on ReadCube.

$32.00

All prices are NET prices.

Fig. 1: Global dynamics of mRNA modifications during the P. falciparum IDC.
Fig. 2: Characterization of the P. falciparum m6A writer complex.
Fig. 3: Knockdown of the PfMT-A70 m6A methyltransferase by CRISPRi.
Fig. 4: Differential m6A methylation during the P. falciparum IDC.
Fig. 5: PfMT-A70 knockdown leads to upregulation of m6A-methylated transcripts.
Fig. 6: Correlation of m6A with mRNA stability and translational efficiency.

Data availability

All sequencing data are accessible on the Gene Expression Omnibus database (https://www.ncbi.nlm.nih.gov/geo) under study accession number GSE123839. Raw sequence data are accessible on the NCBI Sequence Read Archive (SRA) under BioProject accession number PRJNA473770. Proteomics and mRNA modification LC-MS/MS data are deposited at the Chorus database with accession number 1579.

References

  1. World Malaria Report 2018 (World Health Organization, 2018).

  2. Bozdech, Z. et al. The transcriptome of the intraerythrocytic developmental cycle of Plasmodium falciparum. PLoS Biol. 1, 85–100 (2003).

    CAS  Google Scholar 

  3. Scherf, A., Lopez-Rubio, J. J. & Riviere, L. Antigenic variation in Plasmodium falciparum. Annu. Rev. Microbiol. 62, 445–470 (2008).

    CAS  PubMed  Google Scholar 

  4. Kafsack, B. F. C. et al. A transcriptional switch underlies commitment to sexual development in malaria parasites. Nature 507, 248–252 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  5. Salcedo-Amaya, A. M. et al. Dynamic histone H3 epigenome marking during the intraerythrocytic cycle of Plasmodium falciparum. Proc. Natl Acad. Sci. USA 106, 9655–9660 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  6. Kensche, P. R. et al. The nucleosome landscape of Plasmodium falciparum reveals chromatin architecture and dynamics of regulatory sequences. Nucleic Acids Res. 44, 2110–2124 (2016).

    CAS  PubMed  Google Scholar 

  7. Toenhake, C. G. et al. Chromatin accessibility-based characterization of the gene regulatory network underlying Plasmodium falciparum blood-stage development. Cell Host Microbe 23, 557–569 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  8. Ganesan, K. et al. A genetically hard-wired metabolic transcriptome in Plasmodium falciparum fails to mount protective responses to lethal antifolates. PLoS Pathog. 4, e1000214 (2008).

    PubMed  PubMed Central  Google Scholar 

  9. Llinás, M., Bozdech, Z., Wong, E. D., Adai, A. T. & DeRisi, J. L. Comparative whole genome transcriptome analysis of three Plasmodium falciparum strains. Nucleic Acids Res. 34, 1166–1173 (2006).

    PubMed  PubMed Central  Google Scholar 

  10. Painter, H. J. et al. Genome-wide real-time in vivo transcriptional dynamics during Plasmodium falciparum blood-stage development. Nat. Commun. 9, 2656 (2018).

    PubMed  PubMed Central  Google Scholar 

  11. Hughes, K. R., Philip, N., Starnes, G. L., Taylor, S. & Waters, A. P. From cradle to grave: RNA biology in malaria parasites. WIRES RNA 1, 287–303 (2010).

    CAS  PubMed  Google Scholar 

  12. Vembar, S. S., Droll, D. & Scherf, A. Translational regulation in blood stages of the malaria parasite Plasmodium spp.: systems-wide studies pave the way. WIRES RNA 7, 772–792 (2016).

    CAS  PubMed  Google Scholar 

  13. Shock, J. L., Fischer, K. F. & DeRisi, J. L. Whole-genome analysis of mRNA decay in Plasmodium falciparum reveals a global lengthening of mRNA half-life during the intra-erythrocytic development cycle. Genome Biol. 8, R134 (2007).

    PubMed  PubMed Central  Google Scholar 

  14. Foth, B. J., Zhang, N., Mok, S., Preiser, P. R. & Bozdech, Z. Quantitative protein expression profiling reveals extensive post-transcriptional regulation and post-translational modifications in schizont-stage malaria parasites. Genome Biol. 9, R177 (2008).

    PubMed  PubMed Central  Google Scholar 

  15. Caro, F., Ahyong, V., Betegon, M. & DeRisi, J. L. Genome-wide regulatory dynamics of translation in the Plasmodium falciparum asexual blood stages. eLife 3, e04106 (2014).

    PubMed Central  Google Scholar 

  16. Roundtree, I. A., Evans, M. E., Pan, T. & He, C. Dynamic RNA modifications in gene expression regulation. Cell 169, 1187–1200 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  17. Meyer, K. D. & Jaffrey, S. R. Rethinking m6A readers, writers, and erasers. Annu. Rev. Cell Dev. Biol. 33, 319–342 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. Śledź, P. & Jinek, M. Structural insights into the molecular mechanism of the m(6)A writer complex. eLife 5, e18434 (2016).

    PubMed  PubMed Central  Google Scholar 

  19. Wang, Y. et al. N6-methyladenosine modification destabilizes developmental regulators in embryonic stem cells. Nat. Cell Biol. 16, 191–198 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  20. Ping, X.-L. et al. Mammalian WTAP is a regulatory subunit of the RNA N6-methyladenosine methyltransferase. Cell Res. 24, 177–189 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  21. Liu, N. et al. N(6)-methyladenosine-dependent RNA structural switches regulate RNA-protein interactions. Nature 518, 560–564 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  22. Meyer, K. D. et al. 5’ UTR m(6)A promotes cap-independent translation. Cell 163, 999–1010 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  23. Wang, X. et al. N6-methyladenosine-dependent regulation of messenger RNA stability. Nature 505, 117–120 (2014).

    PubMed  Google Scholar 

  24. Patil, D. P., Pickering, B. F. & Jaffrey, S. R. Reading m6A in the transcriptome: m6A-binding proteins. Trends Cell Biol. 28, 113–127 (2018).

    CAS  PubMed  Google Scholar 

  25. Vu, L. P. et al. The N6-methyladenosine (m6A)-forming enzyme METTL3 controls myeloid differentiation of normal hematopoietic and leukemia cells. Nat. Med. 23, 1369–1376 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  26. Lence, T. et al. m6A modulates neuronal functions and sex determination in Drosophila. Nature 540, 242–247 (2016).

    CAS  PubMed  Google Scholar 

  27. Schwartz, S. et al. High-resolution mapping reveals a conserved, widespread, dynamic mRNA methylation program in yeast meiosis. Cell 155, 1409–1421 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  28. Gardner, M. J. et al. Genome sequence of the human malaria parasite Plasmodium falciparum. Nature 419, 498–511 (2002).

    CAS  PubMed  Google Scholar 

  29. Larson, M. H. et al. CRISPR interference (CRISPRi) for sequence-specific control of gene expression. Nat. Protoc. 8, 2180–2196 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  30. Xiao, B. et al. Epigenetic editing by CRISPR/dCas9 in Plasmodium falciparum. Proc. Natl Acad. Sci. USA 116, 255–260 (2019).

    CAS  PubMed  Google Scholar 

  31. Fujita, T. & Fujii, H. Efficient isolation of specific genomic regions and identification of associated proteins by engineered DNA-binding molecule-mediated chromatin immunoprecipitation (enChIP) using CRISPR. Biochem. Biophys. Res. Commun. 439, 132–136 (2013).

    CAS  PubMed  Google Scholar 

  32. Ponts, N. et al. Nucleosome landscape and control of transcription in the human malaria parasite. Genome Res. 20, 228–238 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  33. Bártfai, R. et al. H2A.Z demarcates intergenic regions of the Plasmodium falciparum epigenome that are dynamically marked by H3K9ac and H3K4me3. PLoS Pathog. 6, e1001223 (2010).

    PubMed  PubMed Central  Google Scholar 

  34. Adjalley, S. H., Chabbert, C. D., Klaus, B., Pelechano, V. & Steinmetz, L. M. Landscape and dynamics of transcription Initiation in the malaria parasite Plasmodium falciparum. Cell Rep. 14, 2463–2475 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  35. Linder, B. et al. Single-nucleotide-resolution mapping of m6A and m6Am throughout the transcriptome. Nat. Methods 12, 767–772 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  36. Stevens, A. T., Howe, D. K. & Hunt, A. G. Characterization of mRNA polyadenylation in the apicomplexa. PLoS One 13, e0203317 (2018).

    PubMed  PubMed Central  Google Scholar 

  37. Engel, M. et al. The role of m6A/m-RNA methylation in stress response regulation. Neuron 99, 389–403 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  38. Iyer, L. M., Zhang, D. & Aravind, L. Adenine methylation in eukaryotes: apprehending the complex evolutionary history and functional potential of an epigenetic modification. Bioessays 38, 27–40 (2016).

    CAS  PubMed  Google Scholar 

  39. Schwartz, S. et al. Perturbation of m6A writers reveals two distinct classes of mRNA methylation at internal and 5’ sites. Cell Rep. 8, 284–296 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  40. Wen, J. et al. Zc3h13 regulates nuclear RNA m6A methylation and mouse embryonic stem cell self-renewal. Mol. Cell 69, 1028–1038 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  41. Růžička, K. et al. Identification of factors required for m6A mRNA methylation in Arabidopsis reveals a role for the conserved E3 ubiquitin ligase HAKAI. New Phytol. 215, 157–172 (2017).

    PubMed  PubMed Central  Google Scholar 

  42. Oehring, S. C. et al. Organellar proteomics reveals hundreds of novel nuclear proteins in the malaria parasite Plasmodium falciparum. Genome Biol. 13, R108 (2012).

    PubMed  PubMed Central  Google Scholar 

  43. Yue, Y. et al. VIRMA mediates preferential m6A mRNA methylation in 3’UTR and near stop codon and associates with alternative polyadenylation. Cell Disco. 4, 10 (2018).

    Google Scholar 

  44. Garcia-Campos, M. A. et al. Deciphering the ‘m6A code’ via quantitative profiling of m6A at single-nucleotide resolution. Preprint at https://doi.org/10.1101/571679 (2019).

  45. Ke, S. et al. m6A mRNA modifications are deposited in nascent pre-mRNA and are not required for splicing but do specify cytoplasmic turnover. Genes Dev. 31, 990–1006 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  46. Patil, D. P. et al. M6A RNA methylation promotes XIST-mediated transcriptional repression. Nature 537, 369–373 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  47. Lasonder, E. et al. Integrated transcriptomic and proteomic analyses of P. falciparum gametocytes: molecular insight into sex-specific processes and translational repression. Nucleic Acids Res. 44, 6087–6101 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  48. Lindner, S. E. et al. Extensive transcriptional and translational regulation occur during the maturation of malaria parasite sporozoites. Preprint at https://doi.org/10.1101/642298 (2019).

  49. Cui, L., Lindner, S. & Miao, J. Translational regulation during stage transitions in malaria parasites. Ann. New York Acad. Sci. 1342, 1–9 (2015).

    CAS  Google Scholar 

  50. Lopez-Rubio, J. J., Mancio-Silva, L. & Scherf, A. Genome-wide analysis of heterochromatin associates clonally variant gene regulation with perinuclear repressive centers in malaria parasites. Cell Host Microbe 5, 179–190 (2009).

    CAS  PubMed  Google Scholar 

  51. Vembar, S. S., Macpherson, C. R., Sismeiro, O., Coppée, J.-Y. & Scherf, A. The PfAlba1 RNA-binding protein is an important regulator of translational timing in Plasmodium falciparum blood stages. Genome Biol. 16, 212 (2015).

    PubMed  PubMed Central  Google Scholar 

  52. Ng, C. S. et al. tRNA epitranscriptomics and biased codon are linked to proteome expression in Plasmodium falciparum. Mol. Syst. Biol. 14, e8009 (2018).

    PubMed  PubMed Central  Google Scholar 

  53. Crabb, B. S. et al. Transfection of the human malaria parasite Plasmodium falciparum. Methods Mol. Biol. 270, 263–276 (2004).

    CAS  PubMed  Google Scholar 

  54. Ghorbal, M. et al. Genome editing in the human malaria parasite Plasmodium falciparum using the CRISPR-Cas9 system. Nat. Biotechnol. 32, 819–821 (2014).

    CAS  PubMed  Google Scholar 

  55. Mesén-Ramírez, P. et al. Stable translocation intermediates jam global protein export in Plasmodium falciparum parasites and link the PTEX component EXP2 with translocation activity. PLoS Pathog. 12, e1005618 (2016).

    PubMed  PubMed Central  Google Scholar 

  56. Perkins, D. N., Pappin, D. J., Creasy, D. M. & Cottrell, J. S. Probability-based protein identification by searching sequence databases using mass spectrometry data. Electrophoresis 20, 3551–3567 (1999).

    CAS  PubMed  Google Scholar 

  57. Qi, L. S. et al. Repurposing CRISPR as an RNA-guided platform for sequence-specific control of gene expression. Cell 152, 1173–1183 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  58. MacPherson, C. R. & Scherf, A. Flexible guide-RNA design for CRISPR applications using Protospacer Workbench. Nat. Biotechnol. 33, 805–806 (2015).

    CAS  PubMed  Google Scholar 

  59. Altschul, S. F., Gish, W., Miller, W., Myers, E. W. & Lipman, D. J. Basic local alignment search tool. J. Mol. Biol. 215, 403–410 (1990).

    CAS  PubMed  Google Scholar 

  60. Katoh, K. & Standley, D. M. MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Mol. Biol. Evol. 30, 772–780 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  61. Kearse, M. et al. Geneious Basic: an integrated and extendable desktop software platform for the organization and analysis of sequence data. Bioinformatics 28, 1647–1649 (2012).

    PubMed  PubMed Central  Google Scholar 

  62. Capella-Gutierrez, S., Silla-Martinez, J. M. & Gabaldon, T. trimAl: a tool for automated alignment trimming in large-scale phylogenetic analyses. Bioinformatics 25, 1972–1973 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  63. Darriba, D., Taboada, G. L., Doallo, R. & Posada, D. ProtTest 3: fast selection of best-fit models of protein evolution. Bioinformatics 27, 1164–1165 (2011).

    CAS  PubMed  Google Scholar 

  64. Kumar, S., Stecher, G. & Tamura, K. MEGA7: molecular evolutionary genetics analysis version 7.0 for bigger datasets. Mol. Biol. Evol. 33, 1870–1874 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  65. Oates, M. E. et al. D2P2: database of disordered protein predictions. Nucleic Acids Res. 41, D508–D516 (2013).

    CAS  PubMed  Google Scholar 

  66. Aurrecoechea, C. et al. EuPathDB: the eukaryotic pathogen genomics database resource. Nucleic Acids Res. 45, D581–D591 (2017).

    CAS  PubMed  Google Scholar 

  67. Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25, 1754–1760 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  68. Li, H. et al. The sequence alignment/map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).

    PubMed  PubMed Central  Google Scholar 

  69. Ramírez, F. et al. deepTools2: a next generation web server for deep-sequencing data analysis. Nucleic Acids Res. 44, W160–W165 (2016).

    PubMed  PubMed Central  Google Scholar 

  70. Krzywinski, M. et al. Circos: an information aesthetic for comparative genomics. Genome Res. 19, 1639–1645 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

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

    PubMed  PubMed Central  Google Scholar 

  72. 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 

  73. Heinz, S. et al. Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities. Mol. Cell 38, 576–589 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  74. Marçais, G. & Kingsford, C. A fast, lock-free approach for efficient parallel counting of occurrences of k-mers. Bioinformatics 27, 764–770 (2011).

    PubMed  PubMed Central  Google Scholar 

  75. Crooks, G. E., Hon, G., Chandonia, J.-M. & Brenner, S. E. WebLogo: a sequence logo generator. Genome Res. 14, 1188–1190 (2004).

    CAS  PubMed  PubMed Central  Google Scholar 

  76. Meyer, K. D. et al. Comprehensive analysis of mRNA methylation reveals enrichment in 3′ UTRs and near stop codons. Cell 149, 1635–1646 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  77. Sims, D. et al. CGAT: computational genomics analysis toolkit. Bioinformatics 30, 1290–1291 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  78. Anders, S., Pyl, P. T. & Huber, W. HTSeq–a Python framework to work with high-throughput sequencing data. Bioinformatics 31, 166–169 (2015).

    CAS  PubMed  Google Scholar 

  79. Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).

    PubMed  PubMed Central  Google Scholar 

  80. Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010).

    CAS  PubMed  Google Scholar 

  81. R: A language and environment for statistical computing (R Development Core Team, 2012).

  82. Broadbent, K. M. et al. Strand-specific RNA sequencing in Plasmodium falciparum malaria identifies developmentally regulated long non-coding RNA and circular RNA. BMC Genom. 16, 454 (2015).

    Google Scholar 

Download references

Acknowledgements

We thank A. Claës, C. Scheidig-Benatar and P. Chen for help with parasite culture. Protein mass-spectrometry was performed at the Biopolymers and Proteomics core of The Koch Institute Swanson Biotechnology Center. This work was supported by a European Research Council Advanced Grant (PlasmoSilencing 670301) and the French Parasitology consortium ParaFrap (ANR-11-LABX0024) to A.Scherf. Work in the labs of P.R.P. and P.C.D. was funded by the National Research Foundation Singapore under its Singapore-MIT Alliance for Research and Technology (SMART) Centre, Infectious Disease and Antimicrobial Resistance IRGs. S.B. and J.M.B. were supported by an EMBO fellowship (S.B.: ALTF 1444-2016; J.M.B.: ALTF 180-2015). A.Si. acknowledges financial support from the Singapore-MIT Alliance (SMA) Graduate Fellowships.

Author information

Authors and Affiliations

Authors

Contributions

P.R.P, P.C.D. and A.Sc. conceptualized the project. S.B., J.M.B. and A.Sc. conceived experiments. J.M.B. developed and performed CRISPR interference and dCas9 ChIP-seq experiments. S.B. performed m6A-seq and RT-qPCR experiments. A.Si. performed and analysed LC-MS/MS and protein co-IP experiments. S.B., J.M.B. and T.R. generated constructs, transfectants and parasite material. S.B. performed bioinformatic analyses. P.R.P., P.C.D. and A.Sc. supervised and helped interpret analyses. All authors discussed and approved the manuscript.

Corresponding author

Correspondence to Sebastian Baumgarten.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

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

Supplementary information

Supplementary Information

Supplementary Table 8, Supplementary Figures 1–5 and Supplementary References.

Reporting Summary

Supplementary Tables

Supplementary Tables 1–7 and Supplementary Tables 9–12.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Baumgarten, S., Bryant, J.M., Sinha, A. et al. Transcriptome-wide dynamics of extensive m6A mRNA methylation during Plasmodium falciparum blood-stage development. Nat Microbiol 4, 2246–2259 (2019). https://doi.org/10.1038/s41564-019-0521-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41564-019-0521-7

This article is cited by

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