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Open Access Research

Plant cell wall profiling by fast maximum likelihood reconstruction (FMLR) and region-of-interest (ROI) segmentation of solution-state 2D 1H–13C NMR spectra

Roger A Chylla12*, Rebecca Van Acker34, Hoon Kim12, Ali Azapira1, Purba Mukerjee1, John L Markley2, Véronique Storme34, Wout Boerjan34 and John Ralph12

Author Affiliations

1 DOE Great Lakes Bioenergy Research Center, The Wisconsin Energy Institute, 1552 University Avenue, Madison, WI, 53726, USA

2 Department of Biochemistry, University of Wisconsin Madison, 433 Babcock Drive, Madison, WI, 53706, USA

3 Department of Plant Systems Biology, Flanders Institute for Biotechnology (VIB), Technologiepark 927, Ghent, 9052, Belgium

4 Department of Plant Biotechnology and Bioinformatics, Ghent University, Technologiepark 927, Ghent, 9052, Belgium

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Biotechnology for Biofuels 2013, 6:45  doi:10.1186/1754-6834-6-45

Published: 26 April 2013

Abstract

Background

Interest in the detailed lignin and polysaccharide composition of plant cell walls has surged within the past decade partly as a result of biotechnology research aimed at converting biomass to biofuels. High-resolution, solution-state 2D 1H–13C HSQC NMR spectroscopy has proven to be an effective tool for rapid and reproducible fingerprinting of the numerous polysaccharides and lignin components in unfractionated plant cell wall materials, and is therefore a powerful tool for cell wall profiling based on our ability to simultaneously identify and comparatively quantify numerous components within spectra generated in a relatively short time. However, assigning peaks in new spectra, integrating them to provide relative component distributions, and producing color-assigned spectra, are all current bottlenecks to the routine use of such NMR profiling methods.

Results

We have assembled a high-throughput software platform for plant cell wall profiling that uses spectral deconvolution by Fast Maximum Likelihood Reconstruction (FMLR) to construct a mathematical model of the signals present in a set of related NMR spectra. Combined with a simple region of interest (ROI) table that maps spectral regions to NMR chemical shift assignments of chemical entities, the reconstructions can provide rapid and reproducible fingerprinting of numerous polysaccharide and lignin components in unfractionated cell wall material, including derivation of lignin monomer unit (S:G:H) ratios or the so-called SGH profile. Evidence is presented that ROI-based amplitudes derived from FMLR provide a robust feature set for subsequent multivariate analysis. The utility of this approach is demonstrated on a large transgenic study of Arabidopsis requiring concerted analysis of 91 ROIs (including both assigned and unassigned regions) in the lignin and polysaccharide regions of almost 100 related 2D 1H–13C HSQC spectra.

Conclusions

We show that when a suitable number of replicates are obtained per sample group, the correlated patterns of enriched and depleted cell wall components can be reliably and objectively detected even prior to multivariate analysis. The analysis methodology has been implemented in a publicly-available, cross-platform (Windows/Mac/Linux), web-enabled software application that enables researchers to view and publish detailed annotated spectra in addition to summary reports in simple spreadsheet data formats. The analysis methodology is not limited to studies of plant cell walls but is amenable to any NMR study where ROI segmentation techniques generate meaningful results.

Please see Research Article: http://www.biotechnologyforbiofuels.com/content/6/1/46/ webcite.

Keywords:
Lignin composition; Spectral deconvolution; Maximum likelihood; NMR spectroscopy; Multivariate data analysis