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

Yamazawa, Akira; Iikura, Tomohiro; Shino, Amiu; Date, Yasuhiro; Kikuchi, Jun

doi:10.3390/molecules18089021

Cited by 38 publications

(47 citation statements)

References 39 publications

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“…These results indicate that 13 C-acetic acid and -propionic acid were metabolized and synthesized by Clostridia and Mollicutes. Using correlation analysis, we previously demonstrated that the levels of 13 C-acetic acid and -propionic acid increased as cellulose was degraded, suggesting that the generation of 13 C-labeled organic acids was caused by the anaerobic digestion of 13 C-cellulose [5]. Considering our current findings, we suggest that 13 C-BC was digested by clostridial species and Mollicutes accompanied by the production of acetic acid and propionic acid in the anaerobic microbial ecosystem.…”

Section: Resultssupporting

confidence: 59%

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Cellulose Digestion and Metabolism Induced Biocatalytic Transitions in Anaerobic Microbial Ecosystems

et al. 2013

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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.

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Section: Resultssupporting

confidence: 59%

“…Solution-state NMR data were reduced by subdividing the spectra and were processed and normalized for statistical analysis, as described previously [5]. PCA of the binned data was performed using “R” software according to a previous study [48].…”

Section: Methodsmentioning

confidence: 99%

Cellulose Digestion and Metabolism Induced Biocatalytic Transitions in Anaerobic Microbial Ecosystems

et al. 2013

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“…The extracted samples were measured on an AVANCE II 700 spectrometer (Bruker BioSpin GmbH, Rheinstetten, Germany). The 1 H-NMR spectra were observed using a Bruker standard pulse program "p3919gp" with solvent suppression by the WATERGATE sequence [29,30]. A 90 • pulse angle was used, and the spectral parameters for the large-scale dataset were as follows: number of data points, 32 k; spectral width, 9804 Hz; acquisition time, 1.67 s; delay time, 2.5 s; and number of scans, 64.…”

Section: Nmr Spectroscopymentioning

confidence: 99%

Large-Scale Evaluation of Major Soluble Macromolecular Components of Fish Muscle from a Conventional 1H-NMR Spectral Database

et al. 2020

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Conventional proton nuclear magnetic resonance ( 1 H-NMR) has been widely used for identification and quantification of small molecular components in food. However, identification of major soluble macromolecular components from conventional 1 H-NMR spectra is difficult. This is because the baseline appearance is masked by the dense and high-intensity signals from small molecular components present in the sample mixtures. In this study, we introduced an integrated analytical strategy based on the combination of additional measurement using a diffusion filter, covariation peak separation, and matrix decomposition in a small-scale training dataset. This strategy is aimed to extract signal profiles of soluble macromolecular components from conventional 1 H-NMR spectral data in a large-scale dataset without the requirement of re-measurement. We applied this method to the conventional 1 H-NMR spectra of water-soluble fish muscle extracts and investigated the distribution characteristics of fish diversity and muscle soluble macromolecular components, such as lipids and collagens. We identified a cluster of fish species with low content of lipids and high content of collagens in muscle, which showed great potential for the development of functional foods. Because this mechanical data processing method requires additional measurement of only a small-scale training dataset without special sample pretreatment, it should be immediately applicable to extract macromolecular signals from accumulated conventional 1 H-NMR databases of other complex gelatinous mixtures in foods.

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“…MNR also provides accurate quantification 10 with inter-institution convertibility 11 . In previous studies, NMR-based metabolic profiling has been applied to plants [12][13][14][15] , animals [16][17][18][19][20] , and microbial [21][22][23][24] systems.…”

Section: Introduction * Junkikuchi@rikenjpmentioning

confidence: 99%

[Dedicated to Prof. T. Okada and Prof. T. Nishioka: data science in chemistry]Visualizing Individual and Region-specific Microbial–metabolite Relations by Important Variable Selection Using Machine Learning Approaches

Tsutsui

Date

Kikuchi

2017

J. Comput. Aided Chem.

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Data mining techniques such as machine learning have greatly advanced the chemical and biological sciences. Especially, technological advances in data mining are anticipated for analyzing big data derived from biological and environmental systems. From this perspective, we analyze the complex metabolic and microbial responses of human skin and the relations among these responses using advanced data mining techniques. To this end, metabolic profiles of human sweats were characterized via multiple NMR spectra, followed by an advanced analytical strategy based on data-driven and machine learning approaches. These methods extracted the important variables of the metabolites associated with microbial community variations. Moreover, the relation between the sweat metabolites and the skin microbes was successfully visualized by correlation-based networks. This analytical strategy promises a versatile and useful approach for big data analyses in various fields of science.

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

Cited by 38 publications

References 39 publications

Cellulose Digestion and Metabolism Induced Biocatalytic Transitions in Anaerobic Microbial Ecosystems

Cellulose Digestion and Metabolism Induced Biocatalytic Transitions in Anaerobic Microbial Ecosystems

Large-Scale Evaluation of Major Soluble Macromolecular Components of Fish Muscle from a Conventional 1H-NMR Spectral Database

[Dedicated to Prof. T. Okada and Prof. T. Nishioka: data science in chemistry]Visualizing Individual and Region-specific Microbial–metabolite Relations by Important Variable Selection Using Machine Learning Approaches

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