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

Abstract

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 bird's-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 13C-Cellulose Degradation in an Anaerobic Microbial Ecosystem

“…C-solid-state NMR data were reduced by subdividing spectra into sequential 0.8 ppm-designated regions between 13 C-chemical shifts of 3-200 ppm. The binned data were statistically evaluated by PCA using "R" software according to a previous study [23,38]. A 2D correlation map was calculated as an asymmetric matrix between 13 C-CP-MAS/NMR data and 1 H-or 13 C-solution-state NMR data using Spearman's rank correlation coefficient according to previous studies [18,23].…”

“…Our previous study characterized the anaerobic degradation of glucose, starch, and cellulose in an ecosystem by determining the metabolic reaction pathway [23]; however, the structural characteristics of cellulose were not evaluated because of the lower sensitivity for detecting carbon. To overcome this obstacle, we prepared 13 C-labeled bacterial cellulose (BC) to monitor the degradation profiles of anaerobic microbial digestion.…”

“…The pellicles of 13 C-BC produced by G. xylinus were converted into powder using an automill machine (Tokken Inc., Chiba, Japan). Powdered 13 C-BC was incubated in stirred tank reactors at a constant temperature of 55 °C for 120 h with thermophilic anaerobic digestion sludge provided by Kajima Corporation, Japan as described previously [23]. Two mL of samples were collected from the reactors at 0, 13, 24, 37, 42, 48, 54, 60, 66, 72, 84, 96, 108, 114, and 120 h after addition of the 13 C-BC.…”

[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

“…The signal peak detection and noise eliminations in the aligned spectra were subsequently performed on the SENSI 1D web site (http://emar.riken.jp:3838/SENSI_1D/). The processed metabolic and microbial data were analyzed by PCA and correlation analysis in R software and Microsoft Excel as described in a previous study 44 . The data from the correlation analysis were depicted as a network diagram by gephi (https://gephi.org/) as previously described 45,46 .…”

Non invasive analysis of metabolic changes following nutrient input into diverse fish species, as investigated by metabolic and microbial profiling approaches

“…Microbial DNA extraction was performed according to previous studies with slight modifications (Date et al 2012a). Each DNA sample was amplified by PCR using universal bacterial primers 954f and 1369r targeted to the V6-V8 regions of the 16S rRNA gene according to a previous report (Date et al 2010).…”