Showing papers by "Michael R. Sussman published in 2007"

Method for determining molar concentrations of metabolites in complex solutions from two-dimensional 1H-13C NMR spectra.

Abstract: One-dimensional (1D) 1H nuclear magnetic resonance (NMR) spectroscopy is used extensively for high-throughput analysis of metabolites in biological fluids and tissue extracts. Typically, such spectra are treated as multivariate statistical objects rather than as collections of quantifiable metabolites. We report here a two-dimensional (2D) 1H−13C NMR strategy (fast metabolite quantification, FMQ, by NMR) for identifying and quantifying the ∼40 most abundant metabolites in biological samples. To validate this technique, we prepared mixtures of synthetic compounds and extracts from Arabidopsis thaliana, Saccharomyces cerevisiae, and Medicago sativa. We show that accurate (technical error 2.7%) molar concentrations can be determined in 12 min using our quantitative 2D 1H−13C NMR strategy. In contrast, traditional 1D 1H NMR analysis resulted in 16.2% technical error under nearly ideal conditions. We propose FMQ by NMR as a practical alternative to 1D 1H NMR for metabolomics studies in which 50-mg (extract dry...

Implications of 15N-metabolic labeling for automated peptide identification in Arabidopsis thaliana.

Abstract: We report the first metabolic labeling of Arabidopsis thaliana for proteomic investigation, demonstrating efficient and complete labeling of intact plants. Using a reversed-database strategy, we evaluate the performance of the MASCOT search engine in the analysis of combined natural abundance and 15N-labeled samples. We find that 15N-metabolic labeling appears to increase the ambiguity associated with peptide identifications due in part to changes in the number of isobaric amino acids when the isotopic label is introduced. This is reflected by changes in the distributions of false positive identifications with respect to MASCOT score. However, by determining the nitrogen count from each pair of labeled and unlabeled peptides we may improve our confidence in both heavy and light identifications.

Stable isotope assisted assignment of elemental compositions for metabolomics.

Abstract: Assignment of individual compound identities within mixtures of thousands of metabolites in biological extracts is a major challenge for metabolomic technology. Mass spectrometry offers high sensitivity over a large dynamic range of abundances and molecular weights but is limited in its capacity to discriminate isobaric compounds. In this article, we have extended earlier studies using isotopic labeling for elemental composition elucidation (Rodgers, R. P.; Blumer, E. N.; Hendrickson, C. L.; Marshall, A. G. J. Am. Soc. Mass Spectrom. 2000, 11, 835−40) to limit the formulas consistent with any exact mass measurement by comparing observations of metabolites extracted from Arabidopsis thaliana plants grown with (I) 12C and 14N (natural abundance), (II) 12C and 15N, (III) 13C and 14N, or (IV) 13C and 15N. Unique elemental compositions were determined over a dramatically enhanced mass range by analyzing exact mass measurement data from the four extracts using two methods. In the first, metabolite masses were m...

Prediction of error associated with false-positive rate determination for peptide identification in large-scale proteomics experiments using a combined reverse and forward peptide sequence database strategy.

Abstract: In recent years, a variety of approaches have been developed using decoy databases to empirically assess the error associated with peptide identifications from large-scale proteomics experiments. We have developed an approach for calculating the expected uncertainty associated with false-positive rate determination using concatenated reverse and forward protein sequence databases. After explaining the theoretical basis of our model, we compare predicted error with the results of experiments characterizing a series of mixtures containing known proteins. In general, results from characterization of known proteins show good agreement with our predictions. Finally, we consider how these approaches may be applied to more complicated data sets, as when peptides are separated by charge state prior to false-positive determination.

Using dynamic programming to create isotopic distribution maps from mass spectra

Abstract: Motivation: This article presents a method to identify the isotopic distributions within a mass spectrum using a probabilistic classifier supplemented with dynamic programming. Such a system is needed for a variety of purposes, including generating robust and meaningful features from mass spectra to be used in classification. Results: The primary result of this article is that the dynamic programming approach significantly improves sensitivity, without harming specificity, of a probabilistic classifier for identifying the isotopic distributions. When annotating isotopic distributions where an expert has performed the initial ‘peak-picking’ (removal of noise peaks), the dynamic programming approach gives a true positive rate of 96% and a false positive rate of 0.0%, whereas the classifier alone has a true positive rate of only 47% when the false positive rate is 0.0%. When annotating isotopic distributions in machine peak-picked spectra, which may contain many noise peaks, the dynamic programming approach gives a true positive rate of only 22.0%, but it still keeps a low false positive rate of 1.0% and still outperforms the classifier alone. It is important to note that all these rates are when we require exact matches with the distributions in annotated spectra; in our evaluation a distribution is considered ‘entirely incorrect’ if it is missing even one peak or contains even one extraneous peak. We compared to the THRASH and AID-MS systems using a looser requirement: correctly identifying the distribution that contains the mono-isotopic mass. Under this measure, our dynamic programming approach achieves a true positive rate of 82% and a false positive rate of 1%, which again outperforms the classifier alone. The dynamic programming approach ends up being more conservative than THRASH and AID-MS, yielding both fewer true and false peaks, but the F-score of the dynamic programming approach is significantly better than those of THRASH and AID-MS. All results were obtained with 10-fold cross-validation of 99 sections of mass spectra with a total of 214 hand-annotated isotopic distributions. Availability: Programs are available via http://www.cs.wisc.edu/~mcilwain/IDM Contact: mcilwain@cs.wisc.edu

Use of a gene encoding a histidine protein kinase to create drought resistant plants

Abstract: Plant expression vectors that include promoter sequences operably linked to heterologous ATHK1 polynucleotides, or complements thereof, encoding polypeptides at least 95% identical to SEQ ID NO:26, where the polynucleotides encode polypeptides that confers drought resistance in the plants. Also provided are transgenic plants with increased drought resistance, methods for creating such plants, overexpressors, and underexpressors of ATHK1. Methods for enhancing drought resistance in plants are also provided.