A Transcriptomic Analysis of Higher-Order Ecological Interactions in a Eukaryotic Model Microbial Ecosystem

Area of Science:

• Microbial Ecology
• Systems Biology
• Genomics

Background:

• Nonlinear ecological interactions in microbial ecosystems are understudied, particularly higher-order interactions in eukaryotic microorganisms.
• The wine fermentation ecosystem offers a model to investigate yeast community dynamics and functioning.
• Existing knowledge on wine yeast interactions is limited to pairwise relationships, neglecting multispecies complexities.

Purpose of the Study:

• To investigate nonlinear ecosystem properties by analyzing the transcriptomic response of Saccharomyces cerevisiae in varying culture complexities.
• To identify unique gene expression patterns indicative of higher-order interactions in a tri-species yeast system.
• To understand how microbial community complexity influences ecosystem functioning at a molecular level.

Main Methods:

• Comparative transcriptomic analysis of Saccharomyces cerevisiae in pairwise versus tri-species cultures.
• Utilizing interactive protein-association network visualizations to interpret gene expression data.
• Analyzing extracellular metabolite profiles to identify biotic stress responses.

Main Results:

• Transcriptomic data showed enrichment of pairwise-cultured genes in tri-species cultures, alongside a significant proportion of unique genes indicating a higher-order response.
• Protein-association networks highlighted specific activation of stress response and metabolic adaptation mechanisms during tri-species growth.
• Extracellular metabolite data confirmed a biotic stress response, correlating with observed gene expression differences.

Conclusions:

• This study provides the first molecular evidence for higher-order interactions in a model microbial ecosystem (wine yeast).
• The findings demonstrate that microbial community complexity drives unique cellular responses not predictable from pairwise interactions.
• This research offers molecular targets for future studies aimed at understanding and predicting nonlinear ecosystem dynamics in yeast.