Dynamic metabolic exchange governs a marine algal-bacterial interaction
- Harvard Medical School, United States
- Sungkyunkwan University, United States
- Leibniz-Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH, Germany
- Broad Institute, United States
- Dana-Farber Cancer Institute, United States
- The Hebrew University of Jerusalem, Israel
Abstract
Emiliania huxleyi is a model coccolithophore micro-alga that generates vast blooms in the ocean. Bacteria are not considered among the major factors influencing coccolithophore physiology. Here we show through a laboratory model system that the bacterium Phaeobacter inhibens, a well-studied member of the Roseobacter group, intimately interacts with E. huxleyi. While attached to the algal cell, bacteria initially promote algal growth but ultimately kill their algal host. Both algal growth enhancement and algal death are driven by the bacterially-produced phytohormone indole-3-acetic acid. Bacterial production of indole-3-acetic acid and attachment to algae are significantly increased by tryptophan, which is exuded from the algal cell. Algal death triggered by bacteria involves activation of pathways unique to oxidative stress response and programmed cell death. Our observations suggest that bacteria greatly influence the physiology and metabolism of E. huxleyi. Coccolithophore-bacteria interactions should be further studied in the environment to determine whether they impact micro-algal population dynamics on a global scale.
Data availability
-
Dynamic Metabolic Exchange Governs a Marine Algal-Bacterial InteractionPublicly available at the NCBI Sequence Read Archive (SRA accession: SRP075256).
Article and author information
Author details
Funding
European Molecular Biology Organization (LTF 649-2012)
- Einat Segev
Human Frontier Science Program (LT000061/2013-L)
- Einat Segev
DFG Transregio TRR-51 Roseobacter
- Jörn Petersen
PCMM
- Natasha Barteneva
National Institutes of Health (RR023459)
- Natasha Barteneva
National Institutes of Health (GM086258)
- Jon Clardy
National Institutes of Health (GM58213)
- Roberto Kolter
National Institutes of Health (GM82137)
- Roberto Kolter
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Copyright
© 2016, Segev 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
-
- 7,774
- views
-
- 1,671
- downloads
-
- 205
- 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)
Dynamic metabolic exchange governs a marine algal-bacterial interaction
eLife 5:e17473.
https://doi.org/10.7554/eLife.17473
Further reading
-
- Ecology
Tracking wild pigs with GPS devices reveals how their social interactions could influence the spread of disease, offering new strategies for protecting agriculture, wildlife, and human health.
-
- Ecology
- Neuroscience
In nature, animal vocalizations can provide crucial information about identity, including kinship and hierarchy. However, lab-based vocal behavior is typically studied during brief interactions between animals with no prior social relationship, and under environmental conditions with limited ethological relevance. Here, we address this gap by establishing long-term acoustic recordings from Mongolian gerbil families, a core social group that uses an array of sonic and ultrasonic vocalizations. Three separate gerbil families were transferred to an enlarged environment and continuous 20-day audio recordings were obtained. Using a variational autoencoder (VAE) to quantify 583,237 vocalizations, we show that gerbils exhibit a more elaborate vocal repertoire than has been previously reported and that vocal repertoire usage differs significantly by family. By performing gaussian mixture model clustering on the VAE latent space, we show that families preferentially use characteristic sets of vocal clusters and that these usage preferences remain stable over weeks. Furthermore, gerbils displayed family-specific transitions between vocal clusters. Since gerbils live naturally as extended families in complex underground burrows that are adjacent to other families, these results suggest the presence of a vocal dialect which could be exploited by animals to represent kinship. These findings position the Mongolian gerbil as a compelling animal model to study the neural basis of vocal communication and demonstrates the potential for using unsupervised machine learning with uninterrupted acoustic recordings to gain insights into naturalistic animal behavior.
