Integrated culturing, modeling and transcriptomics uncovers complex interactions and emergent behavior in a three-species synthetic gut community
Abstract
Whereas the composition of the human gut microbiome is well resolved, predictive understanding is still lacking. Here, we followed a bottom-up strategy to explore human gut community dynamics: we established a synthetic community composed of three representative human gut isolates (Roseburia intestinalis L1-82, Faecalibacterium prausnitzii A2-165 and Blautia hydrogenotrophica S5a33) and explored their interactions under well-controlled conditions in vitro. Systematic mono- and pair-wise fermentation experiments confirmed competition for fructose and cross-feeding of formate. We quantified with a mechanistic model how well tri-culture dynamics was predicted from mono-culture data. With the model as reference, we demonstrated that strains grown in co-culture behaved differently than in mono-culture and confirmed their altered behavior at the transcriptional level. In addition, we showed with replicate tri-cultures and simulations that dominance in tri-culture sensitively depended on initial conditions. Our work has important implications for gut microbial community modeling as well as ecological interaction detection from batch cultures.
Data availability
RNA-seq results have been deposited to the Short Read Archive under the study identifier SRP136465 (https://www.ncbi.nlm.nih.gov/sra/SRP136465). Fermentation data have been submitted to Dryad (doi:10.5061/dryad.g83f29f). Source data has been provided for Figures 3 to 6.
-
Data from: Integrated culturing, modeling and transcriptomics uncovers emergent behavior in a synthetic gut communityAvailable at Dryad Digital Repository under a CC0 Public Domain Dedication.
Article and author information
Author details
Funding
Vrije Universiteit Brussel
- Kevin D'hoe
Fonds Wetenschappelijk Onderzoek
- Kevin D'hoe
- Karoline Faust
- Frédéric Moens
- Verónica Lloréns-Rico
Interuniversity Institute of Bioinformatics in Brussels
- Stefan Vet
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Copyright
© 2018, D'hoe 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
-
- 5,744
- views
-
- 976
- downloads
-
- 73
- citations
Views, downloads and citations are aggregated across all versions of this paper published by eLife.
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
- Neuroscience
Accumulating evidence to make decisions is a core cognitive function. Previous studies have tended to estimate accumulation using either neural or behavioral data alone. Here, we develop a unified framework for modeling stimulus-driven behavior and multi-neuron activity simultaneously. We applied our method to choices and neural recordings from three rat brain regions—the posterior parietal cortex (PPC), the frontal orienting fields (FOF), and the anterior-dorsal striatum (ADS)—while subjects performed a pulse-based accumulation task. Each region was best described by a distinct accumulation model, which all differed from the model that best described the animal’s choices. FOF activity was consistent with an accumulator where early evidence was favored while the ADS reflected near perfect accumulation. Neural responses within an accumulation framework unveiled a distinct association between each brain region and choice. Choices were better predicted from all regions using a comprehensive, accumulation-based framework and different brain regions were found to differentially reflect choice-related accumulation signals: FOF and ADS both reflected choice but ADS showed more instances of decision vacillation. Previous studies relating neural data to behaviorally inferred accumulation dynamics have implicitly assumed that individual brain regions reflect the whole-animal level accumulator. Our results suggest that different brain regions represent accumulated evidence in dramatically different ways and that accumulation at the whole-animal level may be constructed from a variety of neural-level accumulators.