An actinobacteria lytic polysaccharide monooxygenase acts on both cellulose and xylan to boost biomass saccharification.

BACKGROUND: Lytic polysaccharide monooxygenases (LPMOs) opened a new horizon for biomass deconstruction. They use a redox mechanism not yet fully understood and the range of substrates initially envisaged to be the crystalline polysaccharides is steadily expanding to non-crystalline ones. RESULTS: The enzyme KpLPMO10A from the actinomycete Kitasatospora papulosa was cloned and overexpressed in Escherichia coli cells in the functional form with native N-terminal. The enzyme can release oxidized species from chitin (C1-type oxidation) and cellulose (C1/C4-type oxidation) similarly to other AA10 members from clade II (subclade A). Interestingly, KpLPMO10A also cleaves isolated xylan (not complexed with cellulose, C4-type oxidation), a rare activity among LPMOs not described yet for the AA10 family. The synergistic effect of KpLPMO10A with Celluclast® and an endo-ß-1,4-xylanase also supports this finding. The crystallographic elucidation of KpLPMO10A at 1.6 Å resolution along with extensive structural analyses did not indicate any evident difference with other characterized AA10 LPMOs at the catalytic interface, tempting us to suggest that these enzymes might also be active on xylan or that the ability to attack both crystalline and non-crystalline substrates involves yet obscure mechanisms of substrate recognition and binding. CONCLUSIONS: This work expands the spectrum of substrates recognized by AA10 family, opening a new perspective for the understanding of the synergistic effect of these enzymes with canonical glycoside hydrolases to deconstruct ligno(hemi)cellulosic biomass.

The Importance of Experimental Design, Quality Assurance, and Control in Plant Metabolomics Experiments.

The output of metabolomics relies to a great extent upon the methods and instrumentation to identify, quantify, and access spatial information on as many metabolites as possible. However, the most modern machines and sophisticated tools for data analysis cannot compensate for inappropriate harvesting and/or sample preparation procedures that modify metabolic composition and can lead to erroneous interpretation of results. In addition, plant metabolism has a remarkable degree of complexity, and the number of identified compounds easily surpasses the number of samples in metabolomics analyses, increasing false discovery risk. These aspects pose a large challenge when carrying out plant metabolomics experiments. In this chapter, we address the importance of a proper experimental design taking into consideration preventable complications and unavoidable factors to achieve success in metabolomics analysis. We also focus on quality control and standardized procedures during the metabolomics workflow.