High resolution fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) for the characterisation of enzymatic processing of commercial lignin

Utilising Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) to track the oxidation of lignin by an alkaliphilic laccase

Lignin is a complex heteroaromatic polymer which is one of the most abundant and diverse biopolymers on the planet. It comprises approximately one third of all woody plant matter, making it an attractive candidate as an alternative, renewable feedstock to petrochemicals to produce fine chemicals. However, the inherent complexity of lignin makes it difficult to analyse and characterise using common analytical techniques, proving a hindrance to the utilisation of lignin as a green chemical feedstock. Herein we outline the tracking of lignin degradation by an alkaliphilic laccase in a semi-quantitative manner using a combined chemical analysis approach using Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) to characterise shifts in chemical diversity and relative abundance of ions, and NMR to highlight changes in the structure of lignin. Specifically, an alkaliphilic laccase was used to degrade an industrially relevant lignin, with compounds such as syringaresinol being almost wholly removed (95%) after 24 hours of treatment. Structural analyses reinforced these findings, indicating a >50% loss of NMR signal relating to β-β linkages, of which syringaresinol is representative. Ultimately, this work underlines a combined analytical approach that can be used to gain a broader semi-quantitative understanding of the enzymatic activity of laccases within a complex, non-model mixture.

Lignin bioconversion based on genome mining for ligninolytic genes in Erwinia billingiae QL-Z3

BACKGROUND

Bioconversion of plant biomass into biofuels and bio-products produces large amounts of lignin. The aromatic biopolymers need to be degraded before being converted into value-added bio-products. Microbes can be environment-friendly and efficiently degrade lignin. Compared to fungi, bacteria have some advantages in lignin degradation, including broad tolerance to pH, temperature, and oxygen and the toolkit for genetic manipulation.

RESULTS

Our previous study isolated a novel ligninolytic bacterial strain Erwinia billingiae QL-Z3. Under optimized conditions, its rate of lignin degradation was 25.24% at 1.5 g/L lignin as the sole carbon source. Whole genome sequencing revealed 4556 genes in the genome of QL-Z3. Among 4428 protein-coding genes are 139 CAZyme genes, including 54 glycoside hydrolase (GH) and 16 auxiliary activity (AA) genes. In addition, 74 genes encoding extracellular enzymes are potentially involved in lignin degradation. Real-time PCR quantification demonstrated that the expression of potential ligninolytic genes were significantly induced by lignin. 8 knock-out mutants and complementary strains were constructed. Disruption of the gene for ELAC_205 (laccase) as well as EDYP_48 (Dyp-type peroxidase), ESOD_1236 (superoxide dismutase), EDIO_858 (dioxygenase), EMON_3330 (monooxygenase), or EMCAT_3587 (manganese catalase) significantly reduced the lignin-degrading activity of QL-Z3 by 47-69%. Heterologously expressed and purified enzymes further confirmed their role in lignin degradation. Fourier transform infrared spectroscopy (FTIR) results indicated that the lignin structure was damaged, the benzene ring structure and groups of macromolecules were opened, and the chemical bond was broken under the action of six enzymes encoded by genes. The abundant enzymatic metabolic products by EDYP_48, ELAC_205 and ESOD_1236 were systematically analyzed via liquid chromatography-mass spectrometry (LC-MS) analysis, and then provide a speculative pathway for lignin biodegradation. Finally, The activities of ligninolytic enzymes from fermentation supernatant, namely, LiP, MnP and Lac were 367.50 U/L, 839.50 U/L, and 219.00 U/L by orthogonal optimization.

CONCLUSIONS

Our findings provide that QL-Z3 and its enzymes have the potential for industrial application and hold great promise for the bioconversion of lignin into bioproducts in lignin valorization.

Mass spectrometry-based methods for the advanced characterization and structural analysis of lignin: A review

Lignin is currently one of the most promising biologically derived resources, due to its abundance and application in biofuels, materials and conversion to value aromatic chemicals. The need to better characterize and understand this complex biopolymer has led to the development of many different analytical approaches, several of which involve mass spectrometry and subsequent data analysis. This review surveys the most important analytical methods for lignin involving mass spectrometry, first looking at methods involving gas chromatography, liquid chromatography and then continuing with more contemporary methods such as matrix assisted laser desorption ionization and time-of-flight-secondary ion mass spectrometry. Following that will be techniques that directly ionize lignin mixtures-without chromatographic separation-using softer atmospheric ionization techniques that leave the lignin oligomers intact. Finally, ultra-high resolution mass analyzers such as FT-ICR have enabled lignin analysis without major sample preparation and chromatography steps. Concurrent with an increase in the resolution of mass spectrometers, there have been a wealth of complementary data analyses and visualization methods that have allowed researchers to probe deeper into the "lignome" than ever before. These approaches extract trends such as compound series and even important analytical information about lignin substructures without performing lignin degradation either chemically or during MS analysis. These innovative methods are paving the way for a more comprehensive understanding of this important biopolymer, as we seek more sustainable solutions for our human species' energy and materials needs.

Enzymatic hydrolysis of corn stover lignin by laccase, lignin peroxidase, and manganese peroxidase

Lignin of high purity and structural integrity was isolated from the enzymatic residue of corn stover. Degradation of the lignin by laccase, lignin peroxidase, and manganese peroxidase was investigated. Structural changes in the lignin after degradation were characterized by scanning electron microscopy, nitrogen adsorption and Fourier transform infrared spectroscopy, and the enzymatic products were systematically analyzed by gas chromatography mass spectrometry. The highest percentage of lignin degradation was obtained with a mixture of three enzymes (25.79%): laccase (Lac), the starting enzyme of the mixed enzyme reaction, worked with lignin peroxidase (LiP), and manganese peroxidase (MnP) to further degrade lignin. This degradation destroyed the macromolecular structure of lignin, broke its key chemical bonds, and opened benzene rings, thus producing more acidic compounds. This study elucidated the concept of degrading lignin from corn stover using the Lac, LiP and MnP enzymes synergistically, thus providing a theoretical basis for the biodegradation of lignin.

Characterization of Lignin Compounds at the Molecular Level: Mass Spectrometry Analysis and Raw Data Processing

Lignin is the second most abundant natural biopolymer, which is a potential alternative to conventional fossil fuels. It is also a promising material for the recovery of valuable chemicals such as aromatic compounds as well as an important biomarker for terrestrial organic matter. Lignin is currently produced in large quantities as a by-product of chemical pulping and cellulosic ethanol processes. Consequently, analytical methods are required to assess the content of valuable chemicals contained in these complex lignin wastes. This review is devoted to the application of mass spectrometry, including data analysis strategies, for the elemental and structural elucidation of lignin products. We describe and critically evaluate how these methods have contributed to progress and trends in the utilization of lignin in chemical synthesis, materials, energy, and geochemistry.

Contributions to Lignomics: Stochastic Generation of Oligomeric Lignin Structures for Interpretation of MALDI-FT-ICR-MS Results

The lack of standards to identify oligomeric molecules is a challenge for the analysis of complex organic mixtures. High-resolution mass spectrometry-specifically, Fourier-transform ion cyclotron resonance mass spectrometry (FT-ICR MS)-offers new opportunities for analysis of oligomers with the assignment of formulae (C_x H_y O_z ) to detected peaks. However, matching a specific structure to a given formula remains a challenge due to the inability of FT-ICR MS to distinguish between isomers. Additional separation techniques and other analyses (e.g., NMR spectroscopy) coupled with comparison of results to those from pure compounds is one route for assignment of MS peaks. Unfortunately, this strategy may be impractical for complete analysis of complex, heterogeneous samples. In this study we use computational stochastic generation of lignin oligomers to generate a molecular library for supporting the assignment of potential candidate structures to compounds detected during FT-ICR MS analysis. This approach may also be feasible for other macromolecules beyond lignin.

Petroleomics: Tools, Challenges, and Developments

The detailed molecular characterization of petroleum-related samples by mass spectrometry, often referred to as petroleomics, continues to present significant analytical challenges. As a result, petroleomics continues to be a driving force for the development of new ultrahigh resolution instrumentation, experimental methods, and data analysis procedures. Recent advances in ionization, resolving power, mass accuracy, and the use of separation methods, have allowed for record levels of compositional detail to be obtained for petroleum-related samples. To address the growing size and complexity of the data generated, vital software tools for data processing, analysis, and visualization continue to be developed. The insights gained impact upon the fields of energy and environmental science and the petrochemical industry, among others. In addition to advancing the understanding of one of nature's most complex mixtures, advances in petroleomics methodologies are being adapted for the study of other sample types, resulting in direct benefits to other fields.

A classification of liquid chromatography mass spectrometry techniques for evaluation of chemical composition and quality control of traditional medicines

Natural products (NPs) and traditional medicines (TMs) are used for treatment of various diseases and also to develop new drugs. However, identification of drug leads within the immense biodiversity of living organisms is a challenging task that requires considerable time, labor, and computational resources as well as the application of modern analytical instruments. LC-MS platforms are widely used for both drug discovery and quality control of TMs and food supplements. Moreover, a large dataset generated during LC-MS analysis contains valuable information that could be extracted and handled by means of various data mining and statistical tools. Novel sophisticated LC-MS based approaches are being introduced every year. Therefore, this review is prepared for the scientists specialized in pharmacognosy and analytical chemistry of NPs as well as working in related areas, in order to navigate them in the world of diverse LC-MS based techniques and strategies currently employed for NP discovery and dereplication, quality control, pattern recognition and sample comparison, and also in targeted and untargeted metabolomic studies. The suggested classification system includes the following LC-MS based procedures: elemental composition determination, isotopic fine structure analysis, mass defect filtering, de novo identification, clustering of the compounds in Molecular Networking (MN), diagnostic fragment ion (or neutral loss) filtering, manual dereplication using MS/MS data, database-assisted peak annotation, annotation of spectral trees, MS fingerprinting, feature extraction, bucketing of LC-MS data, peak profiling, predicted metabolite screening, targeted quantification of biomarkers, quantitative analysis of multi-component system, construction of chemical fingerprints, multi-targeted and untargeted metabolite profiling.