Environmental fluctuations reshape an unexpected diversity-disturbance relationship in a microbial community
Abstract
Environmental disturbances have long been theorized to play a significant role in shaping the diversity and composition of ecosystems. However, an inability to specify the characteristics of a disturbance experimentally has produced an inconsistent picture of diversity-disturbance relationships (DDRs). Here, using a high-throughput programmable culture system, we subjected a soil-derived bacterial community to dilution disturbance profiles with different intensities (mean dilution rates), applied either constantly or with fluctuations of different frequencies. We observed an unexpected U-shaped relationship between community diversity and disturbance intensity in the absence of fluctuations. Adding fluctuations increased community diversity and erased the U-shape. All our results are well-captured by a Monod consumer resource model, which also explains how U-shaped DDRs emerge via a novel 'niche flip' mechanism. Broadly, our combined experimental and modeling framework demonstrates how distinct features of an environmental disturbance can interact in complex ways to govern ecosystem assembly and offers strategies for reshaping the composition of microbiomes.
Data availability
All sequencing data is deposited in the Sequence Read Archive (SRA) accessible with a BioProject accession code PRJNA719465. Agar plate images are deposited on Figshare accessible at doi.org/10.6084/m9.figshare.15117558. Computer code used to run eVOLVER experiments and for theoretical modeling is available at github.com/khalillab. All other datasets required to produce the results in the current study are included as supplemental data. Source data files have been provided.
-
DDR64 Petri Dish Photosfigshare, https://doi.org/10.6084/m9.figshare.15117558.v1.
Article and author information
Author details
Funding
Defense Advanced Research Projects Agency (HR001115C0091)
- Ahmad S Khalil
Defense Advanced Research Projects Agency (HR001117S0029)
- Ahmad S Khalil
Simons Foundation (542385)
- Jeff Gore
National Institute of General Medical Sciences (R01GM102311)
- Jeff Gore
National Institute of Biomedical Imaging and Bioengineering (R01EB027793)
- Ahmad S Khalil
National Institutes of Health (DP2AI131083)
- Ahmad S Khalil
National Science Foundation (MCB-1350949)
- Ahmad S Khalil
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Reviewing Editor
- Wenying Shou, University College London, United Kingdom
Version history
- Preprint posted: July 29, 2020 (view preprint)
- Received: February 2, 2021
- Accepted: August 27, 2021
- Accepted Manuscript published: September 3, 2021 (version 1)
- Version of Record published: September 23, 2021 (version 2)
Copyright
© 2021, Mancuso 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
-
- 2,418
- Page views
-
- 282
- Downloads
-
- 22
- Citations
Article citation count generated by polling the highest count across the following sources: Crossref, PubMed Central, Scopus.
Download links
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)
Further reading
-
- Computational and Systems Biology
- Immunology and Inflammation
Intravital microscopy has revolutionized live-cell imaging by allowing the study of spatial–temporal cell dynamics in living animals. However, the complexity of the data generated by this technology has limited the development of effective computational tools to identify and quantify cell processes. Amongst them, apoptosis is a crucial form of regulated cell death involved in tissue homeostasis and host defense. Live-cell imaging enabled the study of apoptosis at the cellular level, enhancing our understanding of its spatial–temporal regulation. However, at present, no computational method can deliver robust detection of apoptosis in microscopy timelapses. To overcome this limitation, we developed ADeS, a deep learning-based apoptosis detection system that employs the principle of activity recognition. We trained ADeS on extensive datasets containing more than 10,000 apoptotic instances collected both in vitro and in vivo, achieving a classification accuracy above 98% and outperforming state-of-the-art solutions. ADeS is the first method capable of detecting the location and duration of multiple apoptotic events in full microscopy timelapses, surpassing human performance in the same task. We demonstrated the effectiveness and robustness of ADeS across various imaging modalities, cell types, and staining techniques. Finally, we employed ADeS to quantify cell survival in vitro and tissue damage in mice, demonstrating its potential application in toxicity assays, treatment evaluation, and inflammatory dynamics. Our findings suggest that ADeS is a valuable tool for the accurate detection and quantification of apoptosis in live-cell imaging and, in particular, intravital microscopy data, providing insights into the complex spatial–temporal regulation of this process.
-
- Computational and Systems Biology
The microbial community composition in the human gut has a profound effect on human health. This observation has lead to extensive use of microbiome therapies, including over-the-counter ‘probiotic’ treatments intended to alter the composition of the microbiome. Despite so much promise and commercial interest, the factors that contribute to the success or failure of microbiome-targeted treatments remain unclear. We investigate the biotic interactions that lead to successful engraftment of a novel bacterial strain introduced to the microbiome as in probiotic treatments. We use pairwise genome-scale metabolic modeling with a generalized resource allocation constraint to build a network of interactions between taxa that appear in an experimental engraftment study. We create induced sub-graphs using the taxa present in individual samples and assess the likelihood of invader engraftment based on network structure. To do so, we use a generalized Lotka-Volterra model, which we show has strong ability to predict if a particular invader or probiotic will successfully engraft into an individual’s microbiome. Furthermore, we show that the mechanistic nature of the model is useful for revealing which microbe-microbe interactions potentially drive engraftment.