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

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 (1)H–(13)C 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 (1)H–(13)C 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/

Observation and characterization of inactive photosystem II reaction centers

Photosystem II (PS II) is a polypeptide complex which operates in series with the cytochrome $b/f$ complex and photosystem I to perform light-driven transport of electrons from H\sb2O to NADP and of protons from the outside to the inside of the thylakoid membrane. Studies of the turnover rate of the PS II reaction center from higher plants reveals that about one-third of photosystem II reaction centers inactive in energy transduction. Steady-state turnover rates of O\sb2 evolution indicates that active PS II reaction centers turn over at rates of about 250 e-/s but that inactive centers appear to turn over at rates 1000 fold slower, about 2 e-/s.Inactive centers can be observed from measurements of the flash-induced electrochromic shift and the fluorescence yield in thylakoid membranes (in vitro) and intact leaves (in vivo) from spinach (Spinacia oleracea L.). Additional evidence for inactive PS II complexes in spinach leaves is provided by chlorophyll a fluorescence induction and fluorescence decay measurements which can be used to monitor the oxidation kinetics of Q\sb{\rm A}, the primary quinone acceptor of photosystem II.The temperature and pH insensitivity of the kinetic activity of the inactive complexes is consistent with the notion that PS II inactive centers are not in dynamic equilibrium with active centers but are, in fact, a biochemically distinct population of reaction centers existing in parallel with active PS II centers.Measurements of the electrochromic shift and chlorophyll a fluorescence at various dark times after light adaptation suggest that inactive centers are converted in the light into a population of PS II reaction centers, PS II(y), which do not give rise to an observable electrochromic shift or to chlorophyll a fluorescence but instead dissipate absorbed excitation energy non-radiatively. One possibility for the role of PS II(y) centers in photosynthesis is that they dissipate excess excitation energy from the PS II antenna system as a mechanism to prevent photoinhibition. Though this idea is currently speculative, it is noteworthy that in all species of higher plants surveyed thus far (spinach, pea, corn, sorghum, blackeyed pea, cotton, sunflower, soybean, and pea) evidence has been found for the existence of inactive centers. (Abstract shortened with permission of author.)U of I OnlyETDs are only available to UIUC Users without author permissio

Cation effects on the substrate and spin equilibria of cytochrome P-450

Thesis (B.S.) in Biochemistry--University of Illinois at Urbana-Champaign, 1985.Bibliography: leaf 39.U of I OnlyTheses restricted to UIUC community onl

Deconvolution of Two-Dimensional NMR Spectra by Fast Maximum Likelihood Reconstruction: Application to Quantitative Metabolomics

We have developed an algorithm called fast maximum likelihood reconstruction (FMLR) that performs spectral deconvolution of 1D–2D NMR spectra for the purpose of accurate signal quantification. FMLR constructs the simplest time-domain model (e.g., the model with the fewest number of signals and parameters) whose frequency spectrum matches the visible regions of the spectrum obtained from identical Fourier processing of the acquired data. We describe the application of FMLR to quantitative metabolomics and demonstrate the accuracy of the method by analysis of complex, synthetic mixtures of metabolites and liver extracts. The algorithm demonstrates greater accuracy (0.5–5.0% error) than peak height analysis and peak integral analysis with greatly reduced operator intervention. FMLR has been implemented in a Java-based framework that is available for download on multiple platforms and is interoperable with popular NMR display and processing software. Two-dimensional ¹H–¹³C spectra of mixtures can be acquired with acquisition times of 15 min and analyzed by FMLR in the range of 2–5 min per spectrum to identify and quantify constituents present at concentrations of 0.2 mM or greater

Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset