Solid-, solution-, and gas-state NMR monitoring of ¹³C-cellulose degradation in an anaerobic microbial ecosystem

Granulation of Lithium-Ion Sieves Using Biopolymers: A Review

The high demand for lithium (Li) relates to clean, renewable storage devices and the advent of electric vehicles (EVs). The extraction of Li ions from aqueous media calls for efficient adsorbent materials with various characteristics, such as good adsorption capacity, good selectivity, easy isolation of the Li-loaded adsorbents, and good recovery of the adsorbed Li ions. The widespread use of metal-based adsorbent materials for Li ions extraction relates to various factors: (i) the ease of preparation via inexpensive and facile templation techniques, (ii) excellent selectivity for Li ions in a matrix, (iii) high recovery of the adsorbed ions, and (iv) good cycling performance of the adsorbents. However, the use of nano-sized metal-based Lithium-ion sieves (LISs) is limited due to challenges associated with isolating the loaded adsorbent material from the aqueous media. The adsorbent granulation process employing various binding agents (e.g., biopolymers, synthetic polymers, and inorganic materials) affords composite functional particles with modified morphological and surface properties that support easy isolation from the aqueous phase upon adsorption of Li ions. Biomaterials (e.g., chitosan, cellulose, alginate, and agar) are of particular interest because their structural diversity renders them amenable to coordination interactions with metal-based LISs to form three-dimensional bio-composite materials. The current review highlights recent progress in the use of biopolymer binding agents for the granulation of metal-based LISs, along with various crosslinking strategies employed to improve the mechanical stability of the granules. The study reviews the effects of granulation and crosslinking on adsorption capacity, selectivity, isolation, recovery, cycling performance, and the stability of the LISs. Adsorbent granulation using biopolymer binders has been reported to modify the uptake properties of the resulting composite materials to varying degrees in accordance with the surface and textural properties of the binding agent. The review further highlights the importance of granulation and crosslinking for improving the extraction process of Li ions from aqueous media. This review contributes to manifold areas related to industrial application of LISs, as follows: (1) to highlight recent progress in the granulation and crosslinking of metal-based adsorbents for Li ions recovery, (2) to highlight the advantages, challenges, and knowledge gaps of using biopolymer-based binders for granulation of LISs, and finally, (3) to catalyze further research interest into the use of biopolymer binders and various crosslinking strategies to engineer functional composite materials for application in Li extraction industry. Properly engineered extractants for Li ions are expected to offer various cost benefits in terms of capital expenditure, percent Li recovery, and reduced environmental footprint.

Deciphering the contribution of microbial biomass to the properties of dissolved and particulate organic matter in anaerobic digestates

The recalcitrant structures either from substrate or microbial biomass contained in digestates after anaerobic digestion (AD) highly influence digestate valorization. To properly assess the microbial biomass contribution to the digested organic matter (OM), a combination of characterization methods and the use of various substrate types in anaerobic continuous reactors was required. The use of totally biodegradable substrates allowed detecting soluble microbial products via fluorescence spectroscopy at emission wavelengths of 420 and 460 nm while the protein-like signature was enhanced by the whey protein. During reactors' operation, a transfer of complex compounds to the dissolved OM from the particulate OM was observed through fluorescence applied on biochemical fractionation. Consequently, the fluorescence complexity index of the dissolved OM increased from 0.59-0.60 to 1.06-1.07, whereas it decreased inversely for the extractable soluble from the particulate OM from 1.16-1.19 to 0.42-0.54. Accordingly, fluorescence regional integration showed differences among reactors based on visual inspection and orthogonal partial latent structures (OPLS) analysis. Similarly, the impact of the substrate type and operation time on the particulate OM was revealed by ¹³C nuclear magnetic resonance using OPLS, providing a good model (R²X = 0.93 and Q² = 0.8) with a clear time-trend. A high signal resonated at ∼30 ppm attributed to CH₂-groups in the aliphatic chain of lipid-like structure besides carbohydrates intensities at 60-110 ppm distinguished the reactor fed with whey protein from the other, which was mostly biomass related. Indeed, this latter displayed a higher presence of peptidoglycan (δH/C: 1.6-2.0/20-25 ppm) derived from microbial biomass by ¹H-¹³C heteronuclear single-quantum coherence (HSQC) nuclear magnetic resonance. Interestingly, the sample distribution obtained by non-metric multidimensional scaling of bacterial communities resembled the attained using ¹³C NMR properties, opening new research perspectives. Overall, this study discloses the microbial biomass contribution to digestates composition to improve the OM transformation mechanism knowledge.

Structural properties of optically clear bacterial cellulose produced by Komagataeibacter hansenii using arabitol

Bacterial cellulose (BC) exhibits beneficial properties for use in biomedical applications but is limited by its lack of tunable transparency capabilities. To overcome this deficiency, a novel method to synthesize transparent BC materials using an alternative carbon source, namely arabitol, was developed. Characterization of the BC pellicles was performed for yield, transparency, surface morphology, and molecular assembly. Transparent BC was produced using mixtures of glucose and arabitol. Zero percent arabitol pellicles exhibited 25% light transmittance, which increased with increasing arabitol concentration through to 75% light transmittance. While transparency increased, overall BC yield was maintained indicating that the altered transparency may be induced on a micro-scale rather than a macro-scale. Significant differences in fiber diameter and the presence of aromatic signatures were observed. Overall, this research outlines methods for producing BC with tunable optical transparency, while also bringing new insight to insoluble components of exopolymers produced by Komagataeibacter hansenii.

Humic acid enhanced pyrene degradation by Mycobacterium sp. NJS-1

The search for nature-based tools to enhance bioremediation is essential for the sustainable restoration of contaminated ecosystems. Humic acid (HA) is an important component of organic matter in soil and water, but its effect on the microbial degradation of organic pollutants remains unclear. In this study, the biodegradation of pyrene by Mycobacterium sp. NJS-1 with and without HA was investigated. Only around 10.5% of pyrene was biodegraded in the pyrene treatment alone, whereas the addition of HA significantly enhanced biodegradation to the point where over 90% of pyrene was biodegraded. The production of 4,5-dihydropyrene-4,5-diol and phenanthrene-3,4-diol indicated the metabolic pathway via attacking of 4,5-positions of pyrene. Interestingly, 1,2-dimethoxypyrene was detected with the addition of HA, suggesting that HA induced a new ring-opening pathway involving the attack on the 1,2-positions of pyrene. The addition of HA first induced protein self-cleavage behavior with a significant increase in phenylalanine, tyrosine, and tryptophan containing large numbers of COO^- groups. Furthermore, it altered the intracellular and extracellular ultrastructure of bacterial cells, promoting their growth in size and number as well as reducing the space between them. Overall, HA increased the ring-opening positions of pyrene and facilitated its interaction with bacterial cells, thus improving its biodegradability. Building upon the findings of this study to further research is conducive to the sustainable solution of environmental pollution.

The exposome paradigm to predict environmental health in terms of systemic homeostasis and resource balance based on NMR data science

The environment, from microbial ecosystems to recycled resources, fluctuates dynamically due to many physical, chemical and biological factors, the profile of which reflects changes in overall state, such as environmental illness caused by a collapse of homeostasis. To evaluate and predict environmental health in terms of systemic homeostasis and resource balance, a comprehensive understanding of these factors requires an approach based on the "exposome paradigm", namely the totality of exposure to all substances. Furthermore, in considering sustainable development to meet global population growth, it is important to gain an understanding of both the circulation of biological resources and waste recycling in human society. From this perspective, natural environment, agriculture, aquaculture, wastewater treatment in industry, biomass degradation and biodegradable materials design are at the forefront of current research. In this respect, nuclear magnetic resonance (NMR) offers tremendous advantages in the analysis of samples of molecular complexity, such as crude bio-extracts, intact cells and tissues, fibres, foods, feeds, fertilizers and environmental samples. Here we outline examples to promote an understanding of recent applications of solution-state, solid-state, time-domain NMR and magnetic resonance imaging (MRI) to the complex evaluation of organisms, materials and the environment. We also describe useful databases and informatics tools, as well as machine learning techniques for NMR analysis, demonstrating that NMR data science can be used to evaluate the exposome in both the natural environment and human society towards a sustainable future.

Decomposition Factor Analysis Based on Virtual Experiments throughout Bayesian Optimization for Compost-Degradable Polymers

Bio-based polymers have been considered as an alternative to oil-based materials for their “carbon-neutral” environmentally degrative features. However, degradation is a complex system in which environmental factors and preparation conditions are involved, and the relationship between degradation and these factors/conditions has not yet been clarified. Moreover, an efficient system that addresses multiple degradation factors has not been developed for practical use. Thus, we constructed a decomposition degree predictive model to explore degradation factors based on analytical data and experimental conditions. The predictive model was constructed by machine learning using a dataset. The objective variable was the molecular weight, and the explanatory variables were the moisture content in a compost environment, degradation period, degree of crystallinity pre-experiment, and features of solid-state nuclear magnetic resonance spectra. The good accuracy of this predictive model was confirmed by statistical variables. The moisture content in the compost environment was a critical factor for considering initial degradation; specific scores revealed the contribution of degradation factors. Furthermore, the optimum decomposition degree, various analytical values, and experimental conditions were predictable when this predictive model was combined with Bayesian optimization. Information obtained from virtual experiments is expected to promote the material design and development of bio-based plastics.

Signal Deconvolution and Generative Topographic Mapping Regression for Solid-State NMR of Multi-Component Materials

Solid-state nuclear magnetic resonance (ssNMR) spectroscopy provides information on native structures and the dynamics for predicting and designing the physical properties of multi-component solid materials. However, such an analysis is difficult because of the broad and overlapping spectra of these materials. Therefore, signal deconvolution and prediction are great challenges for their ssNMR analysis. We examined signal deconvolution methods using a short-time Fourier transform (STFT) and a non-negative tensor/matrix factorization (NTF, NMF), and methods for predicting NMR signals and physical properties using generative topographic mapping regression (GTMR). We demonstrated the applications for macromolecular samples involved in cellulose degradation, plastics, and microalgae such as Euglena gracilis. During cellulose degradation, ¹³C cross-polarization (CP)-magic angle spinning spectra were separated into signals of cellulose, proteins, and lipids by STFT and NTF. GTMR accurately predicted cellulose degradation for catabolic products such as acetate and CO₂. Using these methods, the ¹H anisotropic spectrum of poly-ε-caprolactone was separated into the signals of crystalline and amorphous solids. Forward prediction and inverse prediction of GTMR were used to compute STFT-processed NMR signals from the physical properties of polylactic acid. These signal deconvolution and prediction methods for ssNMR spectra of macromolecules can resolve the problem of overlapping spectra and support macromolecular characterization and material design.

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

Conventional proton nuclear magnetic resonance (¹H-NMR) has been widely used for identification and quantification of small molecular components in food. However, identification of major soluble macromolecular components from conventional ¹H-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 ¹H-NMR spectral data in a large-scale dataset without the requirement of re-measurement. We applied this method to the conventional ¹H-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 ¹H-NMR databases of other complex gelatinous mixtures in foods.

“Pillaring Effects” in Cross-Linked Cellulose Biopolymers: A Study of Structure and Properties

Modified cellulose materials (CLE-4, CLE-1, and CLE-0.5) were prepared by cross-linking with epichlorohydrin (EP), where the products display variable structure, morphology, and thermal stability. Adsorptive probes such as nitrogen gas and phenolic dyes in aqueous solution reveal that cross-linked cellulose has greater accessible surface area (SA) than native cellulose. The results also reveal that the SA of cross-linked cellulose increased with greater EP content, except for CLE-0.5. The attenuation of SA for CLE-0.5 may relate to surface grafting onto cellulose beyond the stoichiometric cellulose and EP ratio since ca. 30% of the hydroxyl groups of cellulose are accessible for cross-linking reaction due to its tertiary fibril nature. Scanning electron microscopy (SEM) results reveal the variable surface roughness and fibre domains of cellulose due to cross-linking. X-ray diffraction (XRD) and 13C NMR spectroscopy indicate that cellulose adopts a one-chain triclinic unit cell structure (P1 space group) with gauche-trans (gt) and trans-gauche (tg) conformations of the glucosyl linkages and hydroxymethyl groups. The structural characterization results reveal that cross-linking of cellulose occurs at the amorphous domains. By contrast, the crystalline domains are preserved according to similar features in the XRD, FTIR, and 13C NMR spectra of cellulose and its cross-linked forms. This study contributes to an improved understanding of the role of cross-linking of native cellulose in its structure and functional properties. Cross-linked cellulose has variable surface functionality, structure, and textural properties that contribute significantly to their unique physicochemical properties over its native form.

Ultra-sonication assisted cross-linking of cellulose polymers

Cross-linked cellulose-epichlorohydrin polymers were synthesized by a conventional heating with stirring (C-EP heating) and a parallel process using ultra-sonication (C-EP sonication) in the presence of aqueous ammonia. Structural characterization of modified cellulose was carried out using FTIR/¹³C solid state NMR spectroscopy and thermal methods (DSC and TGA). Evidence of products with variable textural properties and morphology was supported by nitrogen gas adsorption, solvent swelling, and microscopy (SEM, TEM) results. C-EP sonication possess greater cross-linker content judging by the loss of the cellulose fibril structure which was facilitated by acoustic cavitation effects due to ultra-sonication. Equilibrium sorption studies in aqueous solution with 2-naphthoxy acetic acid (NAA) revealed that C-EP heating had slightly greater sorption capacity than C-EP sonication at alkaline pH. By contrast, C-EP sonication had greater uptake of NAA at acidic pH. Kinetic uptake studies at pH 3 is described by the pseudo-second order model, where the surface sites of C-EP heating became saturated within ca. 75 min; whereas, ca. 350 min occurred for C-EP sonication. This study demonstrates that the yield of sonication assisted cross-linking of cellulose is greater with improved adsorption properties. The study also reveals the utility of sonication assisted synthesis for the valorization and utilization of cellulose modified materials.

Environmental metabolomics with data science for investigating ecosystem homeostasis

A natural ecosystem can be viewed as the interconnections between complex metabolic reactions and environments. Humans, a part of these ecosystems, and their activities strongly affect the environments. To account for human effects within ecosystems, understanding what benefits humans receive by facilitating the maintenance of environmental homeostasis is important. This review describes recent applications of several NMR approaches to the evaluation of environmental homeostasis by metabolic profiling and data science. The basic NMR strategy used to evaluate homeostasis using big data collection is similar to that used in human health studies. Sophisticated metabolomic approaches (metabolic profiling) are widely reported in the literature. Further challenges include the analysis of complex macromolecular structures, and of the compositions and interactions of plant biomass, soil humic substances, and aqueous particulate organic matter. To support the study of these topics, we also discuss sample preparation techniques and solid-state NMR approaches. Because NMR approaches can produce a number of data with high reproducibility and inter-institution compatibility, further analysis of such data using machine learning approaches is often worthwhile. We also describe methods for data pretreatment in solid-state NMR and for environmental feature extraction from heterogeneously-measured spectroscopic data by machine learning approaches.

Visualization of Microfloral Metabolism for Marine Waste Recycling

Marine biomass including fishery products are precious protein resources for human foods and are an alternative to livestock animals in order to reduce the virtual water problem. However, a large amount of marine waste can be generated from fishery products and it is not currently recycled. We evaluated the metabolism of digested marine waste using integrated analytical methods, under anaerobic conditions and the fertilization of abandoned agricultural soils. Dynamics of fish waste digestion revealed that samples of meat and bony parts had similar dynamics under anaerobic conditions in spite of large chemical variations in input marine wastes. Abandoned agricultural soils fertilized with fish waste accumulated some amino acids derived from fish waste, and accumulation of l-arginine and l-glutamine were higher in plant seedlings. Therefore, we have proposed an analytical method to visualize metabolic dynamics for recycling of fishery waste processes.

SpinCouple: Development of a Web Tool for Analyzing Metabolite Mixtures via Two-Dimensional J-Resolved NMR Database

A new Web-based tool, SpinCouple, which is based on the accumulation of a two-dimensional (2D) (1)H-(1)H J-resolved NMR database from 598 metabolite standards, has been developed. The spectra include both J-coupling and (1)H chemical shift information; those are applicable to a wide array of spectral annotation, especially for metabolic mixture samples that are difficult to label through the attachment of (13)C isotopes. In addition, the user-friendly application includes an absolute-quantitative analysis tool. Good agreement was obtained between known concentrations of 20-metabolite mixtures versus the calibration curve-based quantification results obtained from 2D-Jres spectra. We have examined the web tool availability using nine series of biological extracts, obtained from animal gut and waste treatment microbiota, fish, and plant tissues. This web-based tool is publicly available via http://emar.riken.jp/spincpl.

Metabolic dynamics analysis by massive data integration: application to tsunami-affected field soils in Japan

A new metabolic dynamics analysis approach has been developed in which massive data sets from time-series of (1)H and (13)C NMR spectra are integrated in combination with microbial variability to characterize the biomass degradation process using field soil microbial communities. On the basis of correlation analyses that revealed relationships between various metabolites and bacteria, we efficiently monitored the metabolic dynamics of saccharides, amino acids, and organic acids, by assessing time-course changes in the microbial and metabolic profiles during biomass degradation. Specific bacteria were found to support specific steps of metabolic pathways in the degradation process of biomass to short chain fatty acids. We evaluated samples from agricultural and abandoned fields contaminated by the tsunami that followed the Great East earthquake in Japan. Metabolic dynamics and activities in the biomass degradation process differed considerably between soil from agricultural and abandoned fields. In particular, production levels of short chain fatty acids, such as acetate and propionate, which were considered to be produced by soil bacteria such as Sedimentibacter sp. and Coprococcus sp., were higher in the soil from agricultural fields than from abandoned fields. Our approach could characterize soil activity based on the metabolic dynamics of microbial communities in the biomass degradation process and should therefore be useful in future investigations of the environmental effects of natural disasters on soils.

Profiling planktonic biomass using element-specific, multicomponent nuclear magnetic resonance spectroscopy

Planktonic metabolism plays crucial roles in Earth's elemental cycles. Chemical speciation as well as elemental stoichiometry is important for advancing our understanding of planktonic roles in biogeochemical cycles. In this study, a multicomponent solid-state nuclear magnetic resonance (NMR) approach is proposed for chemical speciation of cellular components, using several advanced NMR techniques. Measurements by ssNMR were performed on (13)C and (15)N-labeled Euglena gracilis, a flagellated protist. 3D dipolar-assisted rotational resonance, double-cross-polarization (1)H-(13)C correlation spectroscopy, and (1)H-(13)C solid-state heteronuclear single quantum correlation spectroscopy successively allowed characterization of cellular components. These techniques were then applied to E. gracilis cultured in high and low ammonium media to demonstrate the power of this method for profiling and comparing cellular components. Cellular NMR spectra indicated that ammonium induced both paramylon degradation and amination. Arginine was stored as a nitrogen reserve and ammonium replaced by arginine catabolism via the arginine dihydrolase pathway. (15)N and (31)P cellular ssNMR indicated arginine and polyphosphate accumulation in E. gracilis, respectively. This chemical speciation technique will contribute to environmental research by providing detailed information on environmental chemical properties.

Comparative metabolomic and ionomic approach for abundant fishes in estuarine environments of Japan

Environmental metabolomics or ionomics is widely used to characterize the effects of environmental stressors on the health of aquatic organisms. However, most studies have focused on liver and muscle tissues of fish, and little is known about how the other organs are affected by environmental perturbations and effects such as metal pollutants or eutrophication. We examined the metabolic and mineral profiles of three kinds of abundant fishes in estuarine ecosystem, yellowfin goby, urohaze-goby, and juvenile Japanese seabass sampled from Tsurumi River estuary, Japan. Multivariate analyses, including nuclear magnetic resonance-based metabolomics and inductively coupled plasma optical emission spectrometry-based ionomics approaches, revealed that the profiles were clustered according to differences among body tissues rather than differences in body size, sex, and species. The metabolic and mineral profiles of the muscle and fin tissues, respectively, suggest that these tissues are most appropriate for evaluating environmental perturbations. Such analyses will be highly useful in evaluating the environmental variation and diversity in aquatic ecosystems.

Chemical profiling of Jatropha tissues under different torrefaction conditions: application to biomass waste recovery

Gradual depletion of the world petroleum reserves and the impact of environmental pollution highlight the importance of developing alternative energy resources such as plant biomass. To address these issues, intensive research has focused on the plant Jatropha curcas, which serves as a rich source of biodiesel because of its high seed oil content. However, producing biodiesel from Jatropha generates large amounts of biomass waste that are difficult to use. Therefore, the objective of our research was to analyze the effects of different conditions of torrefaction on Jatropha biomass. Six different types of Jatropha tissues (seed coat, kernel, stem, xylem, bark, and leaf) were torrefied at four different temperature conditions (200°C, 250°C, 300°C, and 350°C), and changes in the metabolite composition of the torrefied products were determined by Fourier transform-infrared spectroscopy and nuclear magnetic resonance analyses. Cellulose was gradually converted to oligosaccharides in the temperature range of 200°C–300°C and completely degraded at 350°C. Hemicellulose residues showed different degradation patterns depending on the tissue, whereas glucuronoxylan efficiently decomposed between 300°C and 350°C. Heat-induced depolymerization of starch to maltodextrin started between 200°C and 250°C, and oligomer sugar structure degradation occurred at higher temperatures. Lignin degraded at each temperature, e.g., syringyl (S) degraded at lower temperatures than guaiacyl (G). Finally, the toxic compound phorbol ester degraded gradually starting at 235°C and efficiently just below 300°C. These results suggest that torrefaction is a feasible treatment for further processing of residual biomass to biorefinery stock or fertilizer.

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.