Integrating metagenomic binning with flux balance analysis to unravel syntrophies in anaerobic CO2 methanation

Abstract

Background Carbon fixation through biological methanation has emerged as a promising technology to produce renewable energy in the context of the circular economy. The anaerobic digestion microbiome is the fundamental biological system operating biogas upgrading and is paramount in power-to-gas conversion. Carbon dioxide (CO2) methanation is frequently performed by microbiota attached to solid supports generating biofilms. Despite the apparent simplicity of the microbial community involved in biogas upgrading, the dynamics behind most of the interspecies interaction remain obscure. To understand the role of the microbial species in CO2 fixation, the biofilm generated during the biogas upgrading process has been selected as a case study. The present work investigates via genome-centric metagenomics, based on a hybrid Nanopore-Illumina approach the biofilm developed on the diffusion devices of four ex situ biogas upgrading reactors. Moreover, genome-guided metabolic reconstruction and flux balance analysis were used to propose a biological role for the dominant microbes. Results The combined microbiome was composed of 59 species, with five being dominant (> 70% of total abundance); the metagenome-assembled genomes representing these species were refined to reach a high level of completeness. Genome-guided metabolic analysis appointed Firmicutes sp. GSMM966 as the main responsible for biofilm formation. Additionally, species interactions were investigated considering their co-occurrence in 134 samples, and in terms of metabolic exchanges through flux balance simulation in a simplified medium. Some of the most abundant species (e.g., Limnochordia sp. GSMM975) were widespread (~ 67% of tested experiments), while others (e.g., Methanothermobacter wolfeii GSMM957) had a scattered distribution. Genome-scale metabolic models of the microbial community were built with boundary conditions taken from the biochemical data and showed the presence of a flexible interaction network mainly based on hydrogen and carbon dioxide uptake and formate exchange. Conclusions Our work investigated the interplay between five dominant species within the biofilm and showed their importance in a large spectrum of anaerobic biogas reactor samples. Flux balance analysis provided a deeper insight into the potential syntrophic interaction between species, especially Limnochordia sp. GSMM975 and Methanothermobacter wolfeii GSMM957. Finally, it suggested species interactions to be based on formate and amino acids exchanges.

Effect of pressure on biomethanation process and spatial stratification of microbial communities in trickle bed reactors under decreasing gas retention time

“…Initial steps of the reads processing included filtering with Trimmomatic (v0.39-1) (Bolger et al, 2014) and BBDuck tool of the BBTools suite (v38.93) (De Bernardini et al, 2022). These steps removed adapters, low-quality fragments (average Phred score ≤ 20) and bacteriophage phi x174 contamination.…”

NEMETEX: a Python software for the visualisation of the network of metabolic exchanges

“…The tool was tested with results obtained from a genome-centric metagenomic analysis performed on samples collected from an anaerobic biogas reactor (De Bernardini et al , 2022). As an illustrative example of the NEMETEX output format, analysis of the butyrate exchanges within the selected microbial community, is shown in figure 1B.…”

A Randomized Placebo Controlled Clinical Trial of a Metabolic Shifting Probiotic, Sugar Shift, for the Treatment of T2DM

“…Many formulas do not contain any prebiotic at all and most do not evaluate or show the analysis of the genetic and metabolic profile of the consortium of bacteria as a working community in the formula. The BiotiQuest™ Sugar Shift (Sugar Shift) preparation, on the other hand, is a formulation designed with the aid of a community-based flux balance analysis model (18,19), coined the BioFlux™ model, an in silico analytical platform for modeling microbial consortia. The computational modeling provides for the design of formulations with a balance of the flow of metabolic inputs and outputs over time to target a specific metabolic output and improved sustainability.…”