1. Computational and Systems Biology
  2. Evolutionary Biology

Evolution and regulation of microbial secondary metabolism

  1. Guillem Santamaria
  2. Chen Liao
  3. Chloe Lindberg
  4. Yanyan Chen
  5. Zhe Wang
  6. Kyu Rhee
  7. Francisco Rodrigues Pinto
  8. Jinyuan Yan  Is a corresponding author
  9. Joao B Xavier  Is a corresponding author
  1. University of Lisboa, Portugal
  2. Memorial Sloan Kettering Cancer Center, United States
  3. Weill Cornell Medical College, United States
  4. Memorial Sloan-Kettering Cancer Center, United States
https://doi.org/10.7554/eLife.76119
Share this article
Cite this article
  1. Guillem Santamaria
  2. Chen Liao
  3. Chloe Lindberg
  4. Yanyan Chen
  5. Zhe Wang
  6. Kyu Rhee
  7. Francisco Rodrigues Pinto
  8. Jinyuan Yan
  9. Joao B Xavier
(2022)
Evolution and regulation of microbial secondary metabolism
eLife 11:e76119.
https://doi.org/10.7554/eLife.76119
  2. Download BibTeX
  3. Download .RIS

Abstract

Microbes have disproportionate impacts on the macroscopic world. This is in part due to their ability to grow to large populations that collectively secrete massive amounts of secondary metabolites and alter their environment. Yet, the conditions favoring secondary metabolism despite the potential costs for primary metabolism remain unclear. Here we investigated the biosurfactants that the bacterium Pseudomonas aeruginosa makes and secretes to decrease the surface tension of surrounding liquid. Using a combination of genomics, metabolomics, transcriptomics, and mathematical modeling we show that the ability to make surfactants from glycerol varies inconsistently across the phylogenetic tree; instead, lineages that lost this ability are also worse at reducing the oxidative stress of primary metabolism on glycerol. Experiments with different carbon sources support a link with oxidative stress that explains the inconsistent distribution across the P. aeruginosa phylogeny and suggests a general principle: P. aeruginosa lineages produce surfactants if they can reduce the oxidative stress produced by primary metabolism and have excess resources, beyond their primary needs, to afford secondary metabolism. These results add a new layer to the regulation of a secondary metabolite unessential for primary metabolism but important to change physical properties of the environments surrounding bacterial populations.

Data availability

Sequencing data have been deposited in SRA, in the bioproject accession number PRJNA253624. Each individual sample has a file accession number listed in supporting table 7 provided. The additional dataset is provided through Dryad.

The following data sets were generated

Article and author information

Author details

  1. Guillem Santamaria

    3BioISI - Biosystems and Integrative Sciences Institute, University of Lisboa, Lisboa, Portugal
    Competing interests
    The authors declare that no competing interests exist.

  2. Chen Liao

    Program for Computational and Systems Biology, Memorial Sloan Kettering Cancer Center, New York, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-8474-1196

  3. Chloe Lindberg

    Program for Computational and Systems Biology, Memorial Sloan Kettering Cancer Center, New York, United States
    Competing interests
    The authors declare that no competing interests exist.

  4. Yanyan Chen

    Program for Computational and Systems Biology, Memorial Sloan Kettering Cancer Center, New York, United States
    Competing interests
    The authors declare that no competing interests exist.

  5. Zhe Wang

    Department of Medicine, Weill Cornell Medical College, New York, United States
    Competing interests
    The authors declare that no competing interests exist.

  6. Kyu Rhee

    Department of Medicine, Weill Cornell Medical College, New York, United States
    Competing interests
    The authors declare that no competing interests exist.

  7. Francisco Rodrigues Pinto

    3BioISI - Biosystems and Integrative Sciences Institute, University of Lisboa, Lisboa, Portugal
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-4217-0054

  8. Jinyuan Yan

    Computational & Systems Biology, Memorial Sloan-Kettering Cancer Center, New York, United States
    For correspondence
    yanj2@mskcc.org
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-2046-5625

  9. Joao B Xavier

    Program for Computational and Systems Biology, Memorial Sloan Kettering Cancer Center, New York, United States
    For correspondence
    xavierj@mskcc.org
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-3592-1689

Funding

National Institutes of Health (U01 AI124275)

  • Joao B Xavier

National Institutes of Health (R01 AI137269)

  • Joao B Xavier

FCT/Portugal (UIDB/04046/2020)

  • Francisco Rodrigues Pinto

FCT/Portugal (UIDP/04046/2020)

  • Francisco Rodrigues Pinto

European Research Council (734790)

  • Francisco Rodrigues Pinto

FCT/Portugal (SFRH/BD/142899/2018)

  • Guillem Santamaria

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

Reviewing Editor

  1. Sara Mitri, University of Lausanne, Switzerland

Version history

  1. Preprint posted: September 3, 2020 (view preprint)
  2. Received: December 4, 2021
  3. Accepted: November 18, 2022
  4. Accepted Manuscript published: November 21, 2022 (version 1)
  5. Version of Record published: November 29, 2022 (version 2)

Copyright

© 2022, Santamaria 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

  • 2,053
    views
  • 335
    downloads
  • 13
    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. Guillem Santamaria
  2. Chen Liao
  3. Chloe Lindberg
  4. Yanyan Chen
  5. Zhe Wang
  6. Kyu Rhee
  7. Francisco Rodrigues Pinto
  8. Jinyuan Yan
  9. Joao B Xavier
(2022)
Evolution and regulation of microbial secondary metabolism
eLife 11:e76119.
https://doi.org/10.7554/eLife.76119

Share this article

https://doi.org/10.7554/eLife.76119

Categories and tags

Further reading

    1. Computational and Systems Biology
    2. Genetics and Genomics

    Spotless, a reproducible pipeline for benchmarking cell type deconvolution in spatial transcriptomics

    Chananchida Sang-aram, Robin Browaeys ... Yvan Saeys
    Research Article

    Spatial transcriptomics (ST) technologies allow the profiling of the transcriptome of cells while keeping their spatial context. Since most commercial untargeted ST technologies do not yet operate at single-cell resolution, computational methods such as deconvolution are often used to infer the cell type composition of each sequenced spot. We benchmarked 11 deconvolution methods using 63 silver standards, 3 gold standards, and 2 case studies on liver and melanoma tissues. We developed a simulation engine called synthspot to generate silver standards from single-cell RNA-sequencing data, while gold standards are generated by pooling single cells from targeted ST data. We evaluated methods based on their performance, stability across different reference datasets, and scalability. We found that cell2location and RCTD are the top-performing methods, but surprisingly, a simple regression model outperforms almost half of the dedicated spatial deconvolution methods. Furthermore, we observe that the performance of all methods significantly decreased in datasets with highly abundant or rare cell types. Our results are reproducible in a Nextflow pipeline, which also allows users to generate synthetic data, run deconvolution methods and optionally benchmark them on their dataset (https://github.com/saeyslab/spotless-benchmark).

    1. Computational and Systems Biology

    Phantasus, a web-application for visual and interactive gene expression analysis

    Maksim Kleverov, Daria Zenkova ... Alexey A Sergushichev
    Research Article

    Transcriptomic profiling became a standard approach to quantify a cell state, which led to accumulation of huge amount of public gene expression datasets. However, both reuse of these datasets or analysis of newly generated ones requires significant technical expertise. Here we present Phantasus - a user-friendly web-application for interactive gene expression analysis which provides a streamlined access to more than 96000 public gene expression datasets, as well as allows analysis of user-uploaded datasets. Phantasus integrates an intuitive and highly interactive JavaScript-based heatmap interface with an ability to run sophisticated R-based analysis methods. Overall Phantasus allows users to go all the way from loading, normalizing and filtering data to doing differential gene expression and downstream analysis. Phantasus can be accessed on-line at https://alserglab.wustl.edu/phantasus or can be installed locally from Bioconductor (https://bioconductor.org/packages/phantasus). Phantasus source code is available at https://github.com/ctlab/phantasus under MIT license.

    1. Computational and Systems Biology
    2. Evolutionary Biology

    CoCoNuTs are a diverse subclass of Type IV restriction systems predicted to target RNA

    Ryan T Bell, Harutyun Sahakyan ... Eugene V Koonin
    Research Article

    A comprehensive census of McrBC systems, among the most common forms of prokaryotic Type IV restriction systems, followed by phylogenetic analysis, reveals their enormous abundance in diverse prokaryotes and a plethora of genomic associations. We focus on a previously uncharacterized branch, which we denote coiled-coil nuclease tandems (CoCoNuTs) for their salient features: the presence of extensive coiled-coil structures and tandem nucleases. The CoCoNuTs alone show extraordinary variety, with three distinct types and multiple subtypes. All CoCoNuTs contain domains predicted to interact with translation system components, such as OB-folds resembling the SmpB protein that binds bacterial transfer-messenger RNA (tmRNA), YTH-like domains that might recognize methylated tmRNA, tRNA, or rRNA, and RNA-binding Hsp70 chaperone homologs, along with RNases, such as HEPN domains, all suggesting that the CoCoNuTs target RNA. Many CoCoNuTs might additionally target DNA, via McrC nuclease homologs. Additional restriction systems, such as Type I RM, BREX, and Druantia Type III, are frequently encoded in the same predicted superoperons. In many of these superoperons, CoCoNuTs are likely regulated by cyclic nucleotides, possibly, RNA fragments with cyclic termini, that bind associated CARF (CRISPR-Associated Rossmann Fold) domains. We hypothesize that the CoCoNuTs, together with the ancillary restriction factors, employ an echeloned defense strategy analogous to that of Type III CRISPR-Cas systems, in which an immune response eliminating virus DNA and/or RNA is launched first, but then, if it fails, an abortive infection response leading to PCD/dormancy via host RNA cleavage takes over.