Inferring characteristics of bacterial swimming in biofilm matrix from time-lapse confocal laser scanning microscopy

  1. Guillaume Ravel
  2. Michel Bergmann
  3. Alain Trubuil
  4. Julien Deschamps
  5. Romain Briandet
  6. Simon Labarthe  Is a corresponding author
  1. INRAE, France
  2. Inria Bordeaux - Sud-Ouest Research Centre, France

Abstract

Biofilms are spatially organized communities of microorganisms embedded in a self-produced organic matrix, conferring to the population emerging properties such as an increased tolerance to the action of antimicrobials. It was shown that some bacilli were able to swim in the exogenous matrix of pathogenic biofilms and to counterbalance these properties. Swimming bacteria can deliver antimicrobial agents in situ, or potentiate the activity of antimicrobial by creating a transient vascularization network in the matrix. Hence, characterizing swimmer trajectories in the biofilm matrix is of particular interest to understand and optimize this new biocontrol strategy in particular, but also more generally to decipher ecological drivers of population spatial structure in natural biofilms ecosystems. In this study, a new methodology is developed to analyze time-lapse confocal laser scanning images to describe and compare the swimming trajectories of bacilli swimmers populations and their adaptations to the biofilm structure. The method is based on the inference of a kinetic model of swimmer populations including mechanistic interactions with the host biofilm. After validation on synthetic data, the methodology is implemented on images of three different species of motile bacillus species swimming in a Staphylococcus aureus biofilm. The fitted model allows to stratify the swimmer populations by their swimming behavior and provides insights into the mechanisms deployed by the micro-swimmers to adapt their swimming traits to the biofilm matrix.

Data availability

Data and codes have been deposited at https://forgemia.inra.fr/bioswimmers/swim-infer.Labarthe, Simon, Ravel, Guillaume, Deschamps, Julien, & Briandet, Romain. (2022). Inferring characteristics of bacterial swimming in biofilm matrix from time-lapse confocal laser scanning microscopy: compagnon code and data. Zenodo. https://doi.org/10.5281/zenodo.6560673

Article and author information

Author details

  1. Guillaume Ravel

    INRAE, Cestas, France
    Competing interests
    The authors declare that no competing interests exist.
  2. Michel Bergmann

    Inria Bordeaux - Sud-Ouest Research Centre, Talence, France
    Competing interests
    The authors declare that no competing interests exist.
  3. Alain Trubuil

    INRAE, Jouy-en-Josas, France
    Competing interests
    The authors declare that no competing interests exist.
  4. Julien Deschamps

    INRAE, Jouy-en-Josas, France
    Competing interests
    The authors declare that no competing interests exist.
  5. Romain Briandet

    INRAE, Jouy-en-Josas, France
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-8123-3492
  6. Simon Labarthe

    INRAE, Cestas, France
    For correspondence
    simon.labarthe@inrae.fr
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-5463-7256

Funding

Mathnum department - INRAe

  • Guillaume Ravel

Agence Nationale de la Recherche (ANR-12-ALID-0006)

  • Romain Briandet

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

Reviewing Editor

  1. Karine A Gibbs, University of California, Berkeley, United States

Publication history

  1. Received: December 19, 2021
  2. Accepted: June 10, 2022
  3. Accepted Manuscript published: June 14, 2022 (version 1)

Copyright

© 2022, Ravel 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

  • 153
    Page views
  • 111
    Downloads
  • 0
    Citations

Article citation count generated by polling the highest count across the following sources: Crossref, PubMed Central, Scopus.

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. Guillaume Ravel
  2. Michel Bergmann
  3. Alain Trubuil
  4. Julien Deschamps
  5. Romain Briandet
  6. Simon Labarthe
(2022)
Inferring characteristics of bacterial swimming in biofilm matrix from time-lapse confocal laser scanning microscopy
eLife 11:e76513.
https://doi.org/10.7554/eLife.76513

Further reading

    1. Computational and Systems Biology
    2. Genetics and Genomics
    Jayashree Kumar et al.
    Research Article Updated

    Splicing is highly regulated and is modulated by numerous factors. Quantitative predictions for how a mutation will affect precursor mRNA (pre-mRNA) structure and downstream function are particularly challenging. Here, we use a novel chemical probing strategy to visualize endogenous precursor and mature MAPT mRNA structures in cells. We used these data to estimate Boltzmann suboptimal structural ensembles, which were then analyzed to predict consequences of mutations on pre-mRNA structure. Further analysis of recent cryo-EM structures of the spliceosome at different stages of the splicing cycle revealed that the footprint of the Bact complex with pre-mRNA best predicted alternative splicing outcomes for exon 10 inclusion of the alternatively spliced MAPT gene, achieving 74% accuracy. We further developed a β-regression weighting framework that incorporates splice site strength, RNA structure, and exonic/intronic splicing regulatory elements capable of predicting, with 90% accuracy, the effects of 47 known and 6 newly discovered mutations on inclusion of exon 10 of MAPT. This combined experimental and computational framework represents a path forward for accurate prediction of splicing-related disease-causing variants.

    1. Computational and Systems Biology
    Mayank Baranwal et al.
    Research Article

    Predicting the dynamics and functions of microbiomes constructed from the bottom-up is a key challenge in exploiting them to our benefit. Current models based on ecological theory fail to capture complex community behaviors due to higher order interactions, do not scale well with increasing complexity and in considering multiple functions. We develop and apply a long short-term memory (LSTM) framework to advance our understanding of community assembly and health-relevant metabolite production using a synthetic human gut community. A mainstay of recurrent neural networks, the LSTM learns a high dimensional data-driven non-linear dynamical system model. We show that the LSTM model can outperform the widely used generalized Lotka-Volterra model based on ecological theory. We build methods to decipher microbe-microbe and microbe-metabolite interactions from an otherwise black-box model. These methods highlight that Actinobacteria, Firmicutes and Proteobacteria are significant drivers of metabolite production whereas Bacteroides shape community dynamics. We use the LSTM model to navigate a large multidimensional functional landscape to design communities with unique health-relevant metabolite profiles and temporal behaviors. In sum, the accuracy of the LSTM model can be exploited for experimental planning and to guide the design of synthetic microbiomes with target dynamic functions.