Amino acid auxotrophies in human gut bacteria are linked to higher microbiome diversity and long-term stability
Access to EGA study: EGAS00001001704 / Lifelines-DEEP
Please briefly describe your study aim and plan in no more than 4000 characters. Include: A. outline of the study design and methodology; B. proposed use of the requested data; C. timeline;
(A) Outline of the study design and methodology
Amino acid auxotrophies are prevalent among bacteria. They can govern ecological dynamics in microbial communities and indicate metabolic cross-feeding interactions among coexisting genotypes. Despite the ecological importance of auxotrophies, their distribution and impact on the diversity and stability of the human gut microbiome remain poorly understood.
Our study uses metabolic modelling to predict amino acid auxotrophies based on isolate- and metagenome-assembled genomes of human gut bacteria. By combining the auxotrophy predictions with genotype abundance data (estimated based on metagenomic data), we can elucidate the distribution of auxotrophies in individual samples and statistically test for microbiome diversity, long-term stability, and host metabolome associations.
(B) Proposed use of the requested data
We kindly request the longitudinal data (Baseline- and Follow-up metagenomes and plasma metabolomes, including age and gender data). Clean metagenomic reads will be mapped to reference genomes from human gut bacteria (https://www.mbiomenet.org/HRGM/) using CoverM to estimate the relative abundance of species-level genome clusters. Combined with auxotrophy predictions, the Lifelines-Data will allow us to test for associations of auxotrophy distribution with long-term (4 years) microbiome stability and host plasma metabolomic profiles.
In collaboration with Prof. Konrad Aden and Prof. Andre Franke, we have performed the above-described analysis already for cohorts from Kiel (Northern Germany). The results are reported in a recent preprint (BioRxiv: https://doi.org/10.1101/2023.03.23.532984). Three reviewers and the editor from the journal where we submitted the manuscript asked us to perform our analysis on an additional longitudinal cohort to reproduce our finding that a higher degree of auxotrophies in the microbiome is linked to higher long-term stability.
(C) Timeline
Quality control+Filtering of metagenomic reads: 1 week
Mapping of QC-reads to reference genomes (HRGM): 1 week
Statistical analysis of mapping statistics with microbiome diversity+stability and associations with metabolomes: 2 weeks
Adding new results to the manuscript for re-submission and a new version for the preprint: 2 weeks.