metaGEM: reconstruction of genome scale metabolic models directly from metagenomes

Abstract

Metagenomic analyses of microbial communities have revealed a large degree of interspecies and intraspecies genetic diversity through the reconstruction of metagenome assembled genomes (MAGs). Yet, metabolic modeling efforts mainly rely on reference genomes as the starting point for reconstruction and simulation of genome scale metabolic models (GEMs), neglecting the immense intra- and inter-species diversity present in microbial communities. Here, we present metaGEM (https://github.com/franciscozorrilla/metaGEM), an end-to-end pipeline enabling metabolic modeling of multi-species communities directly from metagenomes. The pipeline automates all steps from the extraction of context-specific prokaryotic GEMs from MAGs to community level flux balance analysis (FBA) simulations. To demonstrate the capabilities of metaGEM, we analyzed 483 samples spanning lab culture, human gut, plant-associated, soil, and ocean metagenomes, reconstructing over 14,000 GEMs. We show that GEMs reconstructed from metagenomes have fully represented metabolism comparable to isolated genomes. We demonstrate that metagenomic GEMs capture intraspecies metabolic diversity and identify potential differences in the progression of type 2 diabetes at the level of gut bacterial metabolic exchanges. Overall, metaGEM enables FBA-ready metabolic model reconstruction directly from metagenomes, provides a resource of metabolic models, and showcases community-level modeling of microbiomes associated with disease conditions allowing generation of mechanistic hypotheses.

Plastic-Degrading Potential across the Global Microbiome Correlates with Recent Pollution Trends

“…MetaBAT2 v.2.12.1 ( 114 ) and MaxBin2 v2.2.5 ( 115 ) were also used to generate additional draft bin sets. Finally, the three bin sets were dereplicated and reassembled using metaWRAP v1.2.3 ( 116 ) with the parameters -x 10 -c 50 to obtain the final set of MAGs ( 117 ). Default settings were used except where otherwise stated.…”

Keystone species determine the productivity of synthetic microbial biofilm communities

“…The quality of the metabolic models was validated using MEMOTE (Lieven et al, 2020). The metabolic interaction potential and metabolic resource overlap for each community were analyzed using SMETANA (Zelezniak et al, 2015; Zorrilla et al, 2021). The simulated cross-feeding results were summarized as SMETANA score, which estimates the strength of metabolic exchanging (Zelezniak et al, 2015).…”

“…For instance, it has been applied to link taxa to biological functions of interest (Wei et al, 2019), to identify potential biotic interactions (Durán et al, 2018), and to explore habitat differentiation (Barberán et al, 2012). Another emerging method for exploring microbial interactions is genome-scale metabolic modeling, which can provide insights into metabolic interaction potential and metabolic resource overlap in multi-species communities (Zelezniak et al, 2015; Zorrilla et al, 2021). Microbiome network analysis and metabolic modeling could guide the study of SynComs for animal and plant health (Heinken et al, 2021; Poudel et al, 2016).…”

KEMET – A python tool for KEGG Module evaluation and microbial genome annotation expansion

“…Moreover, genome-scale metabolic models (GSMMs) are now starting to be applied on a metagenome scale [3] , [9] . GSMM are directly informed by annotation databases and can be automatically reconstructed using tools like CarveMe [10] or gapseq [11] .…”