Top-down identification of keystone taxa in the microbiome

Guy Amit, Amir Bashan

Research output: Contribution to journalArticlepeer-review

14 Scopus citations

Abstract

Keystone taxa in ecological communities are native taxa that play an especially important role in the stability of their ecosystem. However, we still lack an effective framework for identifying these taxa from the available high-throughput sequencing without the notoriously difficult step of reconstructing the detailed network of inter-specific interactions. In addition, while most microbial interaction models assume pair-wise relationships, it is yet unclear whether pair-wise interactions dominate the system, or whether higher-order interactions are relevant. Here we propose a top-down identification framework, which detects keystones by their total influence on the rest of the taxa. Our method does not assume a priori knowledge of pairwise interactions or any specific underlying dynamics and is appropriate to both perturbation experiments and metagenomic cross-sectional surveys. When applied to real high-throughput sequencing of the human gastrointestinal microbiome, we detect a set of candidate keystones and find that they are often part of a keystone module – multiple candidate keystone species with correlated occurrence. The keystone analysis of single-time-point cross-sectional data is also later verified by the evaluation of two-time-points longitudinal sampling. Our framework represents a necessary advancement towards the reliable identification of these key players of complex, real-world microbial communities.

Original languageEnglish
Article number3951
JournalNature Communications
Volume14
Issue number1
DOIs
StatePublished - 4 Jul 2023

Bibliographical note

Publisher Copyright:
© 2023, The Author(s).

Funding

Our sincere gratitude goes out to Dr. Ruben Garrido-Oter and Dr. Christopher M. Field for their help in providing the data for Supplementary Fig. . We also thank Yang-Yu Liu, Jonathan Friedman, and Nadav Shnerb for their valuable contributions and insights. Lastly, we appreciate the useful comments of Boaz Amit, Dana Ben Porath, and Tal Ben Porath. A.B. thanks the Azrieli Foundation for supporting this research. This research was supported by the Israel Science Foundation (grant no. 1258/21) and the German-Israeli Foundation for Scientific Research and Development (grant no. I-1523-500.15/2021).

FundersFunder number
German-Israeli Foundation for Scientific Research and DevelopmentI-1523-500.15/2021
Israel Science Foundation1258/21
Azrieli Foundation

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