The dual role of amyloid β-sheet interaction sequences in the cell surface properties of FLO11-encoded flocculins in the yeast Saccharomyces cerevisiae

Abstract

Fungal adhesins (Als) or flocculins are family of cell surface proteins that mediate adhesion to diverse biotic and abiotic surfaces. A striking characteristic of Als proteins originally identified in the pathogenic Candida albicans is to form functional amyloids that mediate cis-interaction leading to the formation of adhesin nanodomains and trans- interaction between amyloid sequences of opposing cells. In this report, we show that flocculins encoded by FLO11 in Saccharomyces cerevisiae behave like adhesins in C. albicans. To do so, we show that the formation of nanodomains under an external physical force requires a threshold number of amyloid-forming sequences in the Flo11 protein. Then, using a genome editing approach, we constructed strains expressing variants of the Flo11 protein under the endogenous FLO11 promoter, leading to the demonstration that the loss of amyloid-forming sequences strongly reduces cell-cell interaction but has no effect on either plastic adherence or invasive growth in agar, both phenotypes being dependent on the N- and C-terminal ends of Flo11p. Finally, we show that the location of Flo11 is not altered either by the absence of amyloid-forming sequences or by the removal of the N- or C-terminus of the protein.

Data availability

The raw dataset has been deposited to Dryad and is accessible at https:/doi.org/10/10.5061/dryad.v41ns1rvvThe sequence of the FLO11 gene from the industrial strain used in this study has been deposited at NCBI under the provisional reference number Banklt246107 Seq1MW448340)

The following data sets were generated

Article and author information

Author details

  1. Clara Bouyx

    Toulouse Biotechnology Institute, INSA, Toulouse, France
    Competing interests
    The authors declare that no competing interests exist.
  2. Marion Schiavone

    Toulouse Biotechnology Institute, INSA, Toulouse, France
    Competing interests
    The authors declare that no competing interests exist.
  3. Marie-Ange Teste

    Toulouse Biotechnology Institute, INSA, Toulouse, France
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-9173-9190
  4. Etienne Dague

    LAAS, CNRS, Toulouse, France
    Competing interests
    The authors declare that no competing interests exist.
  5. Nathalie Sieczkowski

    Lallemand, Lallemand SAS, Blagnac, France
    Competing interests
    The authors declare that no competing interests exist.
  6. Anne Julien

    Lallemand, Lallemand SAS, Blagnac, France
    Competing interests
    The authors declare that no competing interests exist.
  7. Jean Marie François

    Toulouse Biotechnology Institute, INSA, Toulouse, France
    For correspondence
    fran_jm@insa-toulouse.fr
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-9884-5535

Funding

Region Occitanie (n{degree sign}09003813)

  • Jean Marie François

Lallemand SAS (SAIC2016/048 and SAIC 2018/010)

  • Jean Marie François

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Copyright

© 2021, Bouyx et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

Metrics

  • 779
    views
  • 117
    downloads
  • 8
    citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. Clara Bouyx
  2. Marion Schiavone
  3. Marie-Ange Teste
  4. Etienne Dague
  5. Nathalie Sieczkowski
  6. Anne Julien
  7. Jean Marie François
(2021)
The dual role of amyloid β-sheet interaction sequences in the cell surface properties of FLO11-encoded flocculins in the yeast Saccharomyces cerevisiae
eLife 10:e68592.
https://doi.org/10.7554/eLife.68592

Share this article

https://doi.org/10.7554/eLife.68592

Further reading

    1. Computational and Systems Biology
    2. Genetics and Genomics
    Daniel Hui, Scott Dudek ... Marylyn D Ritchie
    Research Article

    Apart from ancestry, personal or environmental covariates may contribute to differences in polygenic score (PGS) performance. We analyzed the effects of covariate stratification and interaction on body mass index (BMI) PGS (PGSBMI) across four cohorts of European (N = 491,111) and African (N = 21,612) ancestry. Stratifying on binary covariates and quintiles for continuous covariates, 18/62 covariates had significant and replicable R2 differences among strata. Covariates with the largest differences included age, sex, blood lipids, physical activity, and alcohol consumption, with R2 being nearly double between best- and worst-performing quintiles for certain covariates. Twenty-eight covariates had significant PGSBMI–covariate interaction effects, modifying PGSBMI effects by nearly 20% per standard deviation change. We observed overlap between covariates that had significant R2 differences among strata and interaction effects – across all covariates, their main effects on BMI were correlated with their maximum R2 differences and interaction effects (0.56 and 0.58, respectively), suggesting high-PGSBMI individuals have highest R2 and increase in PGS effect. Using quantile regression, we show the effect of PGSBMI increases as BMI itself increases, and that these differences in effects are directly related to differences in R2 when stratifying by different covariates. Given significant and replicable evidence for context-specific PGSBMI performance and effects, we investigated ways to increase model performance taking into account nonlinear effects. Machine learning models (neural networks) increased relative model R2 (mean 23%) across datasets. Finally, creating PGSBMI directly from GxAge genome-wide association studies effects increased relative R2 by 7.8%. These results demonstrate that certain covariates, especially those most associated with BMI, significantly affect both PGSBMI performance and effects across diverse cohorts and ancestries, and we provide avenues to improve model performance that consider these effects.

    1. Genetics and Genomics
    Sedigheh Delmaghani, Aziz El-Amraoui
    Insight

    The DYRK1A enzyme is a pivotal contributor to frequent and severe episodes of otitis media in Down syndrome, positioning it as a promising target for therapeutic interventions.