Comprehensive Signal Assignment of ¹³C‑Labeled Lignocellulose Using Multidimensional Solution NMR and ¹³C Chemical Shift Comparison with Solid-State NMR

A multidimensional solution NMR method has been developed using various pulse programs including HCCH-COSY and ¹³C-HSQC-NOESY for the structural characterization of commercially available ¹³C labeled lignocellulose from potatoes (<i>Solanum tuberosum</i> L.), chicory (<i>Cichorium intybus</i>), and corn (<i>Zea mays</i>). This new method allowed for 119 of the signals in the ¹³C-HSQC spectrum of lignocelluloses to be assigned and was successfully used to characterize the structures of lignocellulose samples from three plants in terms of their xylan and xyloglucan structures, which are the major hemicelluloses in angiosperm. Furthermore, this new method provided greater insight into fine structures of lignin by providing a high resolution to the aromatic signals of the β-aryl ether and resinol moieties, as well as the diastereomeric signals of the β-aryl ether. Finally, the ¹³C chemical shifts assigned in this study were compared with those from solid-state NMR and indicated the presence of heterogeneous dynamics in the polysaccharides where rigid cellulose and mobile hemicelluloses moieties existed together

Application of a Deep Neural Network to Metabolomics Studies and Its Performance in Determining Important Variables

Deep neural networks (DNNs), which are kinds of the machine learning approaches, are powerful tools for analyzing big sets of data derived from biological and environmental systems. However, DNNs are not applicable to metabolomics studies because they have difficulty in identifying contribution factors, e.g., biomarkers, in constructed classification and regression models. In this paper, we describe an improved DNN-based analytical approach that incorporates an importance estimation for each variable using a mean decrease accuracy (MDA) calculation, which is based on a permutation algorithm; this approach is called DNN-MDA. The performance of the DNN-MDA approach was evaluated using a data set of metabolic profiles derived from yellowfin goby that lived in various rivers throughout Japan. Its performance was compared with that of conventional multivariate and machine learning methods, and the DNN-MDA approach was found to have the best classification accuracy (97.8%) among the examined methods. In addition to this, the DNN-MDA approach facilitated the identification of important variables such as trimethylamine <i>N</i>-oxide, inosinic acid, and glycine, which were characteristic metabolites that contributed to the discrimination of the geographical differences between fish caught in the Kanto region and those caught in other regions. As a result, the DNN-MDA approach is a useful and powerful tool for determining the geographical origins of specimens and identifying their biomarkers in metabolomics studies that are conducted in biological and environmental systems

Selective Signal Detection in Solid-State NMR Using Rotor-Synchronized Dipolar Dephasing for the Analysis of Hemicellulose in Lignocellulosic Biomass

Solid-state dipolar dephasing filtered (DDF)-INADEQUATE experiments were used to detect the hemicellulosic signals of lignocellulosic mixtures; here dipolar dephasing was used as a signal filter to remove signals derived from cellulose. The maximum filtering efficiency was obtained when the dephasing time was adjusted to half the rotor period at a magic-angle spinning (MAS) frequency of 12 kHz, which indicated that the molecular motions in hemicelluloses are faster than those in cellulose. In a DDF-INADEQUATE spectrum of uniformly ¹³C-labeled lignocellulose from corn (<i>Zea mays</i>) collected with a dephasing time of 1/2ν_MAS, the chemical shifts of β-d-xylopyranose (Xyl<i>p</i>) and α-l-arabinofuranose (Ara<i>f</i>) in glucuronoarabinoxylan, the major hemicellulose in the secondary cell walls of the gramineous plant, were assigned

Differences in Cellulosic Supramolecular Structure of Compositionally Similar Rice Straw Affect Biomass Metabolism by Paddy Soil Microbiota

<div><p>Because they are strong and stable, lignocellulosic supramolecular structures in plant cell walls are resistant to decomposition. However, they can be degraded and recycled by soil microbiota. Little is known about the biomass degradation profiles of complex microbiota based on differences in cellulosic supramolecular structures without compositional variations. Here, we characterized and evaluated the cellulosic supramolecular structures and composition of rice straw biomass processed under different milling conditions. We used a range of techniques including solid- and solution-state nuclear magnetic resonance (NMR) and Fourier transform infrared spectroscopy followed by thermodynamic and microbial degradability characterization using thermogravimetric analysis, solution-state NMR, and denaturing gradient gel electrophoresis. These measured data were further analyzed using an “ECOMICS” web-based toolkit. From the results, we found that physical pretreatment of rice straw alters the lignocellulosic supramolecular structure by cleaving significant molecular lignocellulose bonds. The transformation from crystalline to amorphous cellulose shifted the thermal degradation profiles to lower temperatures. In addition, pretreated rice straw samples developed different microbiota profiles with different metabolic dynamics during the biomass degradation process. This is the first report to comprehensively characterize the structure, composition, and thermal degradation and microbiota profiles using the ECOMICS toolkit. By revealing differences between lignocellulosic supramolecular structures of biomass processed under different milling conditions, our analysis revealed how the characteristic compositions of microbiota profiles develop in addition to their metabolic profiles and dynamics during biomass degradation.</p></div

Thermodynamic characterization of biomass under different milling processes observed in TG/DTG analysis.

<p>TG and DTG degradation curves (A) and bar graph of the activation energy required to decompose lignocellulosic components (B) in samples subjected to different milling processes. Dashed lines, TG profiles; solid lines, DTG profiles.</p

Application of Two-Dimensional Nuclear Magnetic Resonance for Signal Enhancement by Spectral Integration Using a Large Data Set of Metabolic Mixtures

Nuclear magnetic resonance (NMR) spectroscopy has tremendous advantages of minimal sample preparation and interconvertibility of data among different institutions; thus, large data sets are frequently acquired in metabolomics studies. Previously, we used a novel analytical strategy, named signal enhancement by spectral integration (SENSI), to overcome the low signal-to-noise ratio (S/N ratio) problem in ¹³C NMR by integration of hundreds of spectra without additional measurements. In this letter, the development of a SENSI 2D method and application to >1000 2D <i>J</i>RES NMR spectra are described. Remarkably, the obtained SENSI 2D spectrum had an approximate 14-fold increase in the S/N ratio and 80–250 additional peaks without any additional measurements. These results suggest that SENSI 2D is a useful method for assigning weak signals and that the use of coefficient of variation values can support the assignment information and extraction of features from the population characteristics among large data sets

Human Metabolic, Mineral, and Microbiota Fluctuations Across Daily Nutritional Intake Visualized by a Data-Driven Approach

Daily intake information is important for an understanding of the metabolic fluctuation of humans exposed to environmental stimuli. However, little investigation has been performed on the variations in dietary intake as an input and the relationship with human fecal, urinary, and salivary metabolic fluctuations as output information triggered by daily dietary intake. In the present study, we describe a data-driven approach for visualizing the daily intake information on a nutritional scale and for evaluating input–output responses under uncontrolled diets in a human study. For the input evaluation of nutritional intake, we collected information about daily dietary intake and converted this information to numeric data of nutritional elements. Furthermore, for the evaluation of output metabolic, mineral, and microbiota responses, we characterized the metabolic, mineral, and microbiota variations of noninvasive human samples of feces, urine, and saliva. The data-driven approach captured significant differences in the fluctuation of intestinal microbiota and some metabolites caused by a high-protein and a high-fat diet in daily life. This approach should contribute to the metabolic assessment of humans affected by environmental and nutritional factors under unlimited and uncontrolled diets

Compositional characterization of biomass under different milling processes using ¹H-NMR spectroscopy.

<p>PCA score plot (A) and loading plot (B) of biomass degradation profiles based on ¹H-NMR spectra of high-molecular-weight extracted components. The loading plot refers to ¹H-¹³C HSQC spectra (see Fig. 5). Open square, FM-; closed triangle, AM₁-; open triangle, AM₂-; closed circle, BM₁-; open circle, BM₂-processed samples; β-D-Xyl<i>p</i>, β-D-xylopyranoside; α-L-Ara<i>f</i>, α-L-arabinofuranoside; α-L-Fuc<i>p</i>, α-L-fucopyranoside; G, guaiacyl; H, <i>p</i>-hydroxyphenyl; <i>p</i>CA, <i>p</i>-coumarate.</p

Degradability characterization observed in NMR analysis.

<p>Metabolic profiles of FM- (A, C) and BM₂-processed samples (B, D) biomass samples evaluated using the PCA score plots (A, B) and loading plots (C, D) during biomass degradation by soil microbiota. Degraded biomass, including carbohydrates (*), in the BM₂-processed sample is assigned from the ¹H-¹³C HSQC NMR spectrum (E). The peaks indicated by numbers (listed in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0066919#pone-0066919-t001" target="_blank">Table 1</a>) were assigned as D-glucuronate, D-xylose, D-arabitol, cellobiose, and maltose.</p

Schematic overview of this study.

<p>The effects of rice straw pretreatment on the cellulosic supramolecular structure and improvements in digestibility of lignocellulosic biomass for paddy soil microbiota were evaluated by physicochemical and biochemical methods. Rice straw samples were powdered using a blender, AM machine, and BM machine (1). Structural, compositional, thermodynamic, and degradability characterization (from 2–1 to 2–4) were performed using multimeasurement techniques such as FTIR, NMR, TG/DTA, and DGGE fingerprinting. Data were analyzed using the ECOMICS web-based toolkit.</p