Akira Yamazawa

Metabolic Sequences of Anaerobic Fermentation on Glucose-Based Feeding Substrates Based on Correlation Analyses of Microbial and Metabolite Profiling

Degradation processes in various biomasses are managed by complex metabolic dynamics created by diverse and extensive interactions and competition in microbial communities and their environments. It is important to develop visualization methods to provide a birds-eye view when characterizing the entire sequential metabolic process in an environmental ecosystem. Here, we describe an approach for the visualization of the metabolic sequences in anaerobic fermentation ecosystems, characterizing the entire metabolic dynamics using a combination of microbial community profiles and metabolic profiles. By evaluating their time-dependent variation, we found that microbial community profiles and metabolite production processes were characteristically affected by the feeding of different glucose-based substrates (glucose, starch, cellulose), although the compositions of the major microbial community and the metabolites detected were likely to be similar in all experiments. This combinatorial approach to variation in microbial communities and metabolic profiles was used successfully to visualize metabolic sequences in anaerobic fermentation ecosystems, in addition to mining candidate microbiota for cellulose degradation. Thus, this approach provides a powerful tool for visualizing and evaluating metabolic sequences within the biomass degradation process in an environmental ecosystem. This is the first report to visualize the entire metabolic dynamic in an anaerobic fermentation ecosystem as metabolic sequences.

Solid-, Solution-, and Gas-state NMR Monitoring of 13 C-Cellulose Degradation in an Anaerobic Microbial Ecosystem

Anaerobic digestion of biomacromolecules in various microbial ecosystems is influenced by the variations in types, qualities, and quantities of chemical components. Nuclear magnetic resonance (NMR) spectroscopy is a powerful tool for characterizing the degradation of solids to gases in anaerobic digestion processes. Here we describe a characterization strategy using NMR spectroscopy for targeting the input solid insoluble biomass, catabolized soluble metabolites, and produced gases. 13C-labeled cellulose produced by Gluconacetobacter xylinus was added as a substrate to stirred tank reactors and gradually degraded for 120 h. The time-course variations in structural heterogeneity of cellulose catabolism were determined using solid-state NMR, and soluble metabolites produced by cellulose degradation were monitored using solution-state NMR. In particular, cooperative changes between the solid NMR signal and 13C-13C/13C-12C isotopomers in the microbial degradation of 13C-cellulose were revealed by a correlation heat map. The triple phase NMR measurements demonstrated that cellulose was anaerobically degraded, fermented, and converted to methane gas from organic acids such as acetic acid and butyric acid.

Cellulose Digestion and Metabolism Induced Biocatalytic Transitions in Anaerobic Microbial Ecosystems

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.

Visualizing microbial dechlorination processes in underground ecosystem by statistical correlation and network analysis approach

Microbial ecosystems are typified by diverse microbial interactions and competition. Consequently, the microbial networks and metabolic dynamics of bioprocesses catalyzed by these ecosystems are highly complex, and their visualization is regarded as essential to bioengineering technology and innovation. Here we describe a means of visualizing the variants in a microbial community and their metabolic profiles. The approach enables previously unidentified bacterial functions in the ecosystems to be elucidated. We investigated the anaerobic bioremediation of chlorinated ethene in a soil column experiment as a case study. Microbial community and dechlorination profiles in the ecosystem were evaluated by denaturing gradient gel electrophoresis (DGGE) fingerprinting and gas chromatography, respectively. Dechlorination profiles were obtained from changes in dechlorination by microbial community (evaluated by data mining methods). Individual microbes were then associated with their dechlorination profiles by heterogenous correlation analysis. Our correlation-based visualization approach enables deduction of the roles and functions of bacteria in the dechlorination of chlorinated ethenes. Because it estimates functions and relationships between unidentified microbes and metabolites in microbial ecosystems, this approach is proposed as a control-logic tool by which to understand complex microbial processes.