PreprintCited by 11Plant-Microbe Interactions and ImmunityPlant Pathogens and Fungal DiseasesPlant Disease Resistance and Genetics

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

Plant-associated fungi co-opt ancient antimicrobials for host manipulation

Open access

Preprint — published as 10.1126/sciadv.aec1406 · Paper page

Abstract

Evolutionary histories of effector proteins secreted by fungal pathogens to mediate plant colonization remain largely elusive. While most functionally characterized effectors modulate plant immunity, recent discoveries have revealed novel functions in targeting host-associated microbiota. We now developed an Antimicrobial Activity Predictor for Effector Candidates (AMAPEC), and identified a wealth of antimicrobial effectors, including many highly conserved ones — suggesting ancient evolutionary origins. Surprisingly, several plant immune-modulating effectors display antimicrobial activity. We propose that these evolved from ancestral antimicrobials while retaining their original functions. In addition to roles in suppressing host immunity, they may manipulate plant microbiota to promote colonization. We argue that microbial antagonism is a fundamental fungal effector function and suggest that fungi repurposed ancient antimicrobials to serve multiple roles during host-pathogen co-evolution.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1101/2024.01.04.574150

Cites

1 of 1 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Flagged first, then by citations

Research works printed but not linked: 80

  1. 2013, Species interactions and competition, Nat. Educ. Knowl., 4, 8
  2. 10.1038/nature09113 · DOI
  3. 2023, Peptide YY: A Paneth cell antimicrobial peptide that maintains Candida gut commensalism, Science (80-.), 381, 502, 10.1126/science.abq3178 · DOI
  4. 1992, Antifungal proteins from plants. Purification, molecular cloning, and antifungal properties of chitinases from maize seed, J. Biol. Chem, 267, 6635, 10.1016/S0021-9258(19)50474-4 · DOI
  5. 2020, Microbiome manipulation by a soil-borne fungal plant pathogen using effector proteins, Nat. Plants, 6, 1365, 10.1038/s41477-020-00799-5 · DOI
  6. 10.1126/sciadv.aaw9969 · DOI
  7. 10.7554/eLife.04266 · DOI
  8. 2023, Proteins released into the plant apoplast by the obligate parasitic protist Albugo selectively repress phyllosphere-associated bacteria, New Phytol, 239, 2320, 10.1111/nph.18995 · DOI
  9. 10.1016/j.tibtech.2011.05.001 · DOI
  10. 10.1016/j.bpj.2011.02.018 · DOI
  11. 10.1038/nature14098 · DOI
  12. 10.1073/pnas.1611173113 · DOI
  13. 10.1038/srep44332 · DOI
  14. 10.1186/1471-2148-10-1 · DOI
  15. 10.1172/JCI114518 · DOI
  16. 10.1074/jbc.M207587200 · DOI
  17. Eitzen, K. , Sengupta, P. , Kroll, S. , Kemen, E. & Doehlemann, G . A fungal member of the Arabidopsis thaliana phyllosphere antagonizes Albugo laibachii via a GH25 lysozyme. Elife 10, 1 (2021). · DOI
  18. 10.1146/annurev-genet-120215-035203 · DOI
  19. 2022, Microbiota manipulation through the secretion of effector proteins is fundamental to the wealth of lifestyles in the fungal kingdom, FEMS Microbiol. Rev, 46, fuac022, 10.1093/femsre/fuac022 · DOI
  20. 2023, Natural compounds of fungal origin with antimicrobial activity— Potential cosmetics applications, Pharmaceuticals, 16, 1200, 10.3390/ph16091200 · DOI
  21. 10.1016/j.femsre.2004.11.005 · DOI
  22. 10.1038/s41396-018-0293-8 · DOI
  23. 10.1016/j.fbr.2017.07.001 · DOI
  24. 10.1038/s41467-020-20241-w · DOI · Paper page
  25. 10.1038/s41467-019-13889-6 · DOI · Paper page
  26. Doehlemann, G. , Ökmen, B. , Zhu, W. & Sharon, A. Plant pathogenic fungi. Microbiol. Spectr. 5, (2017). · DOI
  27. 10.1038/s43705-022-00136-1 · DOI
  28. 10.1101/2023.04.10.536216 · DOI
  29. 10.1073/pnas.2110968118 · DOI
  30. 2023, A highly polymorphic effector protein promotes fungal virulence through suppression of plant-associated Actinobacteria, New Phytol, 237, 944, 10.1111/nph.18576 · DOI
  31. 2018, Characterization of an antimicrobial and phytotoxic ribonuclease secreted by the fungal wheat pathogen Zymoseptoria tritici, New Phytol, 217, 320, 10.1111/nph.14786 · DOI
  32. 2023, A conserved extracellular Ribo1 with broad-spectrum cytotoxic activity enables smut fungi to compete with host-associated bacteria, New Phytol, 240, 1976, 10.1111/nph.19244 · DOI
  33. 2022, The Epichloë festucae antifungal protein Efe-AfpA protects creeping bentgrass (Agrostis stolonifera) from the plant pathogen Clarireedia jacksonii, the causal agent of dollar spot disease, J. Fungi, 8, 1097, 10.3390/jof8101097 · DOI
  34. 2023, Co-evolution within the plant holobiont drives host performance, EMBO Rep, 24, e57455, 10.15252/embr.202357455 · DOI
  35. 10.1038/srep41926 · DOI
  36. 10.1093/bioinformatics/bty179 · DOI
  37. Lin, T.-T. , et al. AI4AMP: an antimicrobial peptide predictor using physicochemical property-based encoding method and deep learning. mSystems 6, (2021). · DOI
  38. 2023, AMP-BERT: Prediction of antimicrobial peptide function based on a BERT model, Protein Sci, 32, e4529, 10.1002/pro.4529 · DOI
  39. 2020, Deep-AmPEP30: Improve short antimicrobial peptides prediction with deep learning, Mol. Ther. - Nucleic Acids, 20, 882, 10.1016/j.omtn.2020.05.006 · DOI
  40. 10.1371/journal.pone.0016968 · DOI
  41. 10.1093/NAR/GKAA991 · DOI
  42. 2019, DRAMP 2.0, an updated data repository of antimicrobial peptides, Sci. Data 2019 61, 6, 1
  43. 10.1093/nar/gkv1278 · DOI
  44. Todd, J. N. A. , Carreón-Anguiano, K. G. , Islas-Flores, I. & Canto-Canché, B . Fungal effectoromics: a world in constant evolution. Int. J. Mol. Sci. 23, 13433 (2022). · DOI
  45. 10.1073/pnas.1119623109 · DOI
  46. 10.1371/journal.pone.0141586 · DOI
  47. 2022, Long reads and Hi-C sequencing illuminate the two-compartment genome of the model arbuscular mycorrhizal symbiont Rhizophagus irregularis, New Phytol, 233, 1097, 10.1111/nph.17842 · DOI
  48. 10.1006/jmbi.1990.9999 · DOI · Paper page
  49. van Kempen, M. et al. Fast and accurate protein structure search with Foldseek. Nat. Biotechnol. 1–4 (2023). · DOI · Paper page
  50. 2023, The antimicrobial peptide database is 20 years old: Recent developments and future directions, Protein Sci, 32, e4778, 10.1002/pro.4778 · DOI
  51. 10.1101/2021.06.03.446934 · DOI
  52. Zheng, J. et al. dbCAN3: automated carbohydrate-active enzyme and substrate annotation. Nucleic Acids Res. gka d328 (2023). · DOI
  53. 10.1093/nar/gkab1045 · DOI · Paper page
  54. 10.1126/science.ade2574 · DOI · Paper page
  55. 10.1038/s41598-018-23276-8 · DOI
  56. 10.1038/nrmicro.2017.76 · DOI
  57. Sanderson, T. , Bileschi, M. L. , Belanger, D. & Colwell, L. J . ProteInfer, deep neural networks for protein functional inference. Elife 12, (2023). · DOI
  58. 10.1038/s41467-022-35677-5 · DOI · Paper page
  59. 10.1126/sci-ence.adf2465 · DOI
  60. 10.1101/2023.11.02.565344 · DOI
  61. 2022, EffectorP 3.0: prediction of apoplastic and cytoplasmic effectors in fungi and oomycetes, Mol. Plant-Microbe Interact, 35, 146, 10.1094/MPMI-08-21-0201-R · DOI
  62. 10.1186/s12917-020-02466-5 · DOI
  63. 2022, SignalP 6.0 predicts all five types of signal peptides using protein language models, Nat. Biotechnol, 40, 1023, 10.1038/s41587-021-01156-3 · DOI
  64. 10.1093/NAR/GKAB1061 · DOI · Paper page
  65. 10.1038/S41592-022-01488-1 · DOI · Paper page
  66. 10.1093/nar/gkv1209 · DOI
  67. 10.1093/nar/gkt1183 · DOI · Paper page
  68. 10.1093/nar/gky1085 · DOI
  69. 10.1093/nar/gkaa1100 · DOI · Paper page
  70. 10.32614/RJ-2015-001 · DOI
  71. 2018, ssbio: a Python framework for structural systems biology, Bioinformatics, 34, 2155, 10.1093/bioinformatics/bty077 · DOI
  72. 10.1186/1471-2105-7-1 · DOI
  73. 2016, PDBparam: online resource for computing structural parameters of proteins, Bioinform. Biol. Insights, 10, 73
  74. 1983, Dictionary of protein secondary structure: pattern recognition of hydrogen-bonded and geometrical features, Biopolym. Orig. Res. Biomol, 22, 2577
  75. 10.1093/nar/gku1028 · DOI
  76. 10.1186/1471-2105-10-1 · DOI
  77. Liang, Y. et al. Research progress of reduced amino acid alphabets in protein analysis and prediction. Comput. Struct. Biotechnol. J. (2022). · DOI
  78. 10.1101/2022.11.22.517562 · DOI
  79. 10.1016/j.cpc.2010.04.018 · DOI
  80. 10.2307/1164588 · DOI · Paper page

Metadata

Authors
Mesny, F., Wolf, V., López-Moral, A., Kraege, A., Punt, W., Park, J., Zhu, J., Sato, Y., & Thomma, B. P.
Affiliations
University of Cologne (DE)
Corresponding author
  • Wolf, Valentina
  • Kraege, Anton
  • Punt, Wilko
  • Thomma, Bart PHJ
Venue
Science Advances
Publisher
openRxiv
Published
2024-01-04
Volume
12
Issue
18
Pages
eaec1406-eaec1406
ISSN
2375-2548 (ISSN-L)
OpenAlex
W7159016525
Cited by
11
References
1
Citations by year
2026: 6 · 2025: 3 · 2024: 2
FWCI
22.19
Type
journal article
Language
en
Open access
Locations
  • Science Advances (publishedVersion, cc-by)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
  • PubMed (publishedVersion)
  • PubMed Central (publishedVersion, cc-by)
Funders
  • Alexander von Humboldt-Stiftung (390686111, EXC 2048/1)
  • Fundación Ramón Areces
  • Deutsche Forschungsgemeinschaft (458090666, EXC-2048, EXC-2048/1, 508411006, ME 6064/1-1, 390686111, 2048/1)
  • Bundesministerium für Bildung und Forschung
  • China Scholarship Council
  • Ministerium für Kultur und Wissenschaft des Landes Nordrhein-Westfalen (NRW Profilbildung ID: PB22-025A)
  • Japan Society for the Promotion of Science
Topics
  • Life Sciences › Agricultural and Biological Sciences › Plant Science › Plant-Microbe Interactions and Immunity
  • Life Sciences › Agricultural and Biological Sciences › Plant Science › Plant Pathogens and Fungal Diseases
  • Life Sciences › Agricultural and Biological Sciences › Plant Science › Plant Disease Resistance and Genetics
Keywords
fungal effectors, plant-associated microbiota, host manipulation, plant immunity, effector evolution, antimicrobial activity prediction, Verticillium dahliae, host-pathogen coevolution, plant colonization, nuclear targeting
MeSH terms
Anti-Infective Agents, Ascomycota, Fungal Proteins, Fungi, Plant Diseases, Plants, Verticillium, Host-Pathogen Interactions, Plant Immunity, Microbiota

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review