Abstract

Anaerobic digestion of highly polymerized biomass by microbial communities present in diverse microbial ecosystems is an indispensable metabolic process for biogeochemical cycling in nature and for industrial activities required to maintain a sustainable society. Therefore, the evaluation of the complicated microbial metabolomics presents a significant challenge. We here describe a comprehensive strategy for characterizing the degradation of highly crystallized bacterial cellulose (BC) that is accompanied by metabolite production for identifying the responsible biocatalysts, including microorganisms and their metabolic functions. To this end, we employed two-dimensional solid- and one-dimensional solution-state nuclear magnetic resonance (NMR) profiling combined with a metagenomic approach using stable isotope labeling. The key components of biocatalytic reactions determined using a metagenomic approach were correlated with cellulose degradation and metabolic products. The results indicate that BC degradation was mediated by cellulases that contain carbohydrate-binding modules and that belong to structural type A. The degradation reactions induced the metabolic dynamics of the microbial community and produced organic compounds, such as acetic acid and propionic acid, mainly metabolized by clostridial species. This combinatorial, functional and structural metagenomic approach is useful for the comprehensive characterization of biomass degradation, metabolic dynamics and their key components in diverse ecosystems.

Highlights

  • Microbial metabolism of highly polymerized biomass in anaerobic environments, known as anaerobic microbial digestion, is one of the most significant processes on Earth [1]

  • We considered that it is feasible to determine the microorganisms involved, as well as the conserved domains of certain proteins, carbohydrate-binding modules (CBMs), in particular, which are associated with the process, to evaluate the degradation of supermolecular structure in bacterial cellulose (BC) induced by the complex microbial community, including unknown and unculturable microorganisms, using their various catalytic enzymes accompanied by their metabolic products in anaerobic ecosystems

  • This study focused on the characterization and elucidation of biocatalytic transitions in the anaerobic digestion process, i.e., the degradation of a supermolecular structure in BC induced by the complex microbial community, including unknown and unculturable microorganisms, using their various catalytic enzymes accompanied by their metabolic products

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Summary

Introduction

Microbial metabolism of highly polymerized biomass in anaerobic environments, known as anaerobic microbial digestion, is one of the most significant processes on Earth [1]. The previous study used an NMR-based metabolomic approach to characterize solution- and gas-state metabolites in combination with the analysis of solid-state BC, because triple-phase NMR spectroscopy is the only method available that analyzes the reactions of solid, liquid and gas phases using a single instrument and characterizes the metabolic dynamics of BC degradation and the production and consumption of short-chain fatty acids and methane This triple-phase NMR approach is a powerful tool for monitoring metabolic conversions of biomass into biogas through short-chain fatty acids by anaerobic digestion, the microbial community and its metabolic functions responsible for the degradation and metabolism of 13C-labeled BC (13C-BC) were not identified [5]. These correlation analyses revealed the relationships between microbial functions (CBMs) and BC degradation and between the microbial community and its metabolic dynamics (short-chain fatty acid production from the BC biomass)

Results and Discussion
Key Components of Biocatalytic Transitions in BC Digestion Process
Relationships between BC Digestion and Biocatalytic Transitions of Proteins
General
NMR Spectroscopy
DNA Extraction and Metagenomic Dataset Preparation
Classification of the Microbial Community and CBMs Using the E-Class Web Tool
Statistical Analysis
Conclusions
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