Cost-efficient and user-friendly 17O/18O labeling procedures of fatty acids using mechanochemistry.

Two mechanochemical procedures for 17O/18O-isotope labeling of fatty acids are reported: a carboxylic acid activation/hydrolysis approach and a saponification approach. The latter route allowed first-time enrichment of important polyunsaturated fatty acids (PUFAs) including docosahexaenoic acid (DHA). Overall, a total of 9 pure labeled products were isolated in high yields (≥80%) and with high enrichment levels (≥37% average labeling of C=O and C-OH carboxylic oxygen atoms), under mild conditions, and in short time (

Quantitative assessment of myocardial perfusion in the detection of significant coronary artery disease: cutoff values and diagnostic accuracy of quantitative [(15)O]H2O PET imaging.

BACKGROUND: Recent studies have demonstrated improved diagnostic accuracy for detecting coronary artery disease (CAD) when myocardial blood flow (MBF) is quantified in absolute terms, but there are no uniformly accepted cutoff values for hemodynamically significant CAD. OBJECTIVES: The goal of this study was to determine cutoff values for absolute MBF and to evaluate the diagnostic accuracy of quantitative [(15)O]H2O positron emission tomography (PET). METHODS: A total of 330 patients underwent both quantitative [(15)O]H2O PET imaging and invasive coronary angiography in conjunction with fractional flow reserve measurements. A stenosis >90% and/or fractional flow reserve ≤0.80 was considered obstructive; a stenosis <30% and/or fractional flow reserve >0.80 was nonobstructive. RESULTS: Hemodynamically significant CAD was diagnosed in 116 (41%) of 281 patients who fulfilled study criteria for CAD. Resting perfusion was 1.00 ± 0.25 and 0.92 ± 0.23 ml/min/g in regions supplied by nonstenotic and significantly stenosed vessels, respectively (p < 0.001). During stress, perfusion increased to 3.26 ± 1.04 ml/min/g and 1.73 ± 0.67 ml/min/g, respectively (p < 0.001). The optimal cutoff values were 2.3 and 2.5 for hyperemic MBF and myocardial flow reserve, respectively. For MBF, these cutoff values showed a sensitivity, specificity, and accuracy for detecting significant CAD of 89%, 84%, and 86%, respectively, at a per-patient level and 87%, 85%, and 85% at a per-vessel level. The corresponding myocardial flow reserve values were 86%, 72%, and 78% (per patient) and 80%, 82%, and 81% (per vessel). Age and sex significantly affected diagnostic accuracy of quantitative PET. CONCLUSIONS: Quantitative MBF measurements with the use of [(15)O]H2O PET provided high diagnostic performance, but both sex and age should be taken into account.

Assessment of enzymatic methods in the δ18O value determination of the L-tyrosine p-hydroxy group for proof of illegal meat and bone meal feeding to cattle.

The δ(18)O value of the p-hydroxy group of L-tyrosine depends on the biosynthesis by plants or animals, respectively. In animal proteins it reflects the diet and is therefore an absolute indicator for illegal feeding with meat and bone meal. The aim of this investigation was to perform the positional (18)O determination on L-tyrosine via a one-step enzymatic degradation. Proteins from plants, herbivores, omnivores, and carnivores were characterized by their δ(13)C, δ(15)N, and δ(18)O values, the latter for normalizing the positional δ(18)O values. Their L-tyrosine was degraded by tyrosine phenol lyase to phenol, analyzed as (2,4,6)-tribromophenol. Degradation by tyrosine decarboxylase yielded tyramine. The δ(18)O values of both analytes corresponded to the trophic levels of their sources but were not identical, probably due to an isotope effect on the tyrosine phenol lyase reaction. Availability of the enzyme, easy control of the reaction, and isolation of the analyte are in favor of tyrosine decarboxylase degradation as a routine method.

Nitrate loading and isotopic signatures in subsurface agricultural drainage systems.

Artificially draining soils using subsurface tiles is a common practice on many agricultural fields. High levels of nitrate-nitrogen (NO-N) are often released from these systems; therefore, knowledge on the sources and processes controlling NO-N in drainage systems is needed. A dual isotope study (δN and δO) was used to investigate three subsurface drainage systems (shallow, conventional, and controlled) in Onslow, Nova Scotia, Canada. The objectives of this study were (i) to identify which drainage system more effectively reduced the NO-N loading, (ii) to examine differences in isotopic signatures under identical nutrient and cropping regimes for a fixed soil type, and (iii) to identify the utility of different drainage systems in controlling nutrient flows. Nitrate concentrations measured ranged from 0.92 to 11.8, from 2.3 to 17.3, and from 2.1 to 19.8 mg L for the shallow, conventional, and controlled drains, respectively. Total NO-N loading from shallow and controlled drains were 20 and 5.6 kg ha, respectively, lower than conventional (39.1 kg ha). The isotopic composition of NO-N for all drainage types appeared to be a mixture of two organic sources (manure and soil organic matter) via the process of nitrification. There was no evidence that denitrification played a significant role in removing NO-N during transport. Overall, shallow drainage reduced NO-N loading but offered no water conservation benefits. Combining the benefits of decreased NO-N loading from shallow systems with water control capability may offer the best solution to reducing nutrient loadings into water systems, achieving optimal crop yield, and decreasing drainage installation costs.

A Bayesian Markov-chain-based heteroscedastic regression model for the analysis of 18O-labeled mass spectra.

To reduce the influence of the between-spectra variability on the results of peptide quantification, one can consider the (18)O-labeling approach. Ideally, with such labeling technique, a mass shift of 4 Da of the isotopic distributions of peptides from the labeled sample is induced, which allows one to distinguish the two samples and to quantify the relative abundance of the peptides. It is worth noting, however, that the presence of small quantities of (16)O and (17)O atoms during the labeling step can cause incomplete labeling. In practice, ignoring incomplete labeling may result in the biased estimation of the relative abundance of the peptide in the compared samples. A Markov model was developed to address this issue (Zhu, Valkenborg, Burzykowski. J. Proteome Res. 9, 2669-2677, 2010). The model assumed that the peak intensities were normally distributed with heteroscedasticity using a power-of-the-mean variance funtion. Such a dependence has been observed in practice. Alternatively, we formulate the model within the Bayesian framework. This opens the possibility to further extend the model by the inclusion of random effects that can be used to capture the biological/technical variability of the peptide abundance. The operational characteristics of the model were investigated by applications to real-life mass-spectrometry data sets and a simulation study.

A Markov-chain model for the analysis of high-resolution enzymatically 18O-labeled mass spectra.

The enzymatic 18O-labeling is a useful quantification technique to account for between-spectrum variability of the results of mass spectrometry experiments. One of the important issues related to the use of the technique is the problem of incomplete labeling of peptide molecules, which may result in biased estimates of the relative peptide abundance. In this manuscript, we propose a Markov-chain model, which takes into account the possibility of incomplete labeling in the estimation of the relative abundance from the observed data. This allows for the use of less precise but faster labeling strategies, which should better fit in the high-throughput proteomic framework. Our method does not require extra experimental steps, as proposed in the approaches developed by Mirgorodskaya et al. (2000), López-Ferrer et al. (2006) and Rao et al. (2005), while it includes the model proposed by Eckel-Passow et al. (2006) as a special case. The method estimates information about the isotopic distribution directly from the observed data and is able to account for biases induced by the different sulphur content in peptides as reported by Johnson and Muddiman (2004). The method is integrated in a statistically sound framework and allows for the calculation of the errors on the parameter estimates based on model theory. In this manuscript, we describe the methodology in a technical matter and assess the properties of the algorithm via a thorough simulation study. The method is also tested on a limited dataset; more intense validation and investigation of the operational characteristics is being scheduled.

SVM model for quality assessment of medium resolution mass spectra from 18O-water labeling experiments.

We describe a method for assessing the quality of mass spectra and improving reliability of relative ratio estimations from (18)O-water labeling experiments acquired from low resolution mass spectrometers. The mass profiles of heavy and light peptide pairs are often affected by artifacts, including coeluting contaminant species, noise signal, instrumental fluctuations in measuring ion position and abundance levels. Such artifacts distort the profiles, leading to erroneous ratio estimations, thus reducing the reliability of ratio estimations in high throughput quantification experiments. We used support vector machines (SVMs) to filter out mass spectra that deviated significantly from expected theoretical isotope distributions. We built an SVM classifier with a decision function that assigns a score to every mass profile based on such spectral features as mass accuracy, signal-to-noise ratio, and differences between experimental and theoretical isotopic distributions. The classifier was trained using a data set obtained from samples of mouse renal cortex. We then tested it on protein samples (bovine serum albumin) mixed in five different ratios of labeled and unlabeled species. We demonstrated that filtering the data using our SVM classifier results in as much as a 9-fold reduction in the coefficient of variance of peptide ratios, thus significantly improving the reliability of ratio estimations.

Assessment of nitrogen and oxygen isotopic fractionation during nitrification and its expression in the marine environment.

Nitrification is a microbially-catalyzed process whereby ammonia (NH(3)) is oxidized to nitrite (NO(2)(-)) and subsequently to nitrate (NO(3)(-)). It is also responsible for production of nitrous oxide (N(2)O), a climatically important greenhouse gas. Because the microbes responsible for nitrification are primarily autotrophic, nitrification provides a unique link between the carbon and nitrogen cycles. Nitrogen and oxygen stable isotope ratios have provided insights into where nitrification contributes to the availability of NO(2)(-) and NO(3)(-), and where it constitutes a significant source of N(2)O. This chapter describes methods for determining kinetic isotope effects involved with ammonia oxidation and nitrite oxidation, the two independent steps in the nitrification process, and their expression in the marine environment. It also outlines some remaining questions and issues related to isotopic fractionation during nitrification.

Microwave-assisted 18O-labeling of proteins catalyzed by formic acid.

Oxygen exchange may occur at carboxyl groups catalyzed by acid. The reaction, however, takes at least several days at room temperature. The long-time exchanging reaction often prevents its application from protein analysis. In this study, an (18)O-labeling method utilizing microwave-assisted acid hydrolysis was developed. After being dissolved in (16)O/(18)O (1:1) water containing 2.5% formic acid, protein samples were exposed to microwave irradiation. LC-MS/MS analysis of the resulted peptide mixtures indicated that oxygen in the carboxyl groups from glutamic acid, aspartic acid, and the C-terminal residues could be efficiently exchanged with (18)O within less than 15 min. The rate of back exchange was so slow that no detectable back exchange could be found during the HPLC run.

Optimization and quality assessment of the post-digestion 18O labeling based on urea for protein denaturation by HPLC/ESI-TOF mass spectrometry.

The post-digestion (18)O labeling method decouples protein digestion and peptide labeling. This method allows labeling conditions to be optimized separately and increases labeling efficiency. A common method for protein denaturation in proteomics is the use of urea. Though some previous studies have used urea-based protein denaturation before post-digestion (18)O labeling, the optimal (18)O labeling conditions in this case have not been yet reported. Present study investigated the effects of urea concentration and pH on the labeling efficiency and obtained an optimized protocol. It was demonstrated that urea inhibited (18)O incorporation depending on concentration. However, a urea concentration between 1 and 2M had minimal effects on labeling. It was also demonstrated that the use of FA to quench the digestion reaction severely affected the labeling efficiency. This study revealed the reason why previous studies gave different optimal pH for labeling. They neglect the effects of different digestion conditions on the labeling conditions. Excellent labeling quality was obtained at the optimized conditions using urea 1-2 M and pH 4.5, 98.4+/-1.9% for a standard protein mixture and 97.2+/-6.2% for a complex biological sample. For a 1:1 mixture analysis of the (16)O- and (18)O-labeled peptides from the same protein sample, the average abundance ratios reached 1.05+/-0.31, demonstrating a good quantitation quality at the optimized conditions. This work will benefit other researchers who pair urea-based protein denaturation with a post-digestion (18)O labeling method.

Markov-chain-based heteroscedastic regression model for the analysis of high-resolution enzymatically 18O-labeled mass spectra.

The enzymatic (18)O-labeling is a useful technique for reducing the influence of the between-spectrum variability on the results of mass-spectrometry experiments. A limitation of the technique is the possibility of an incomplete labeling, which may result in biased estimates of the relative peptide abundance. We propose a Markov-chain-based regression model with heterogeneous residual variance, which corrects for the possible bias. Our method does not require extra experimental steps, as proposed in the approaches proposed previously in the literature. On the other hand, it includes some of the alternative approaches as a special case. Moreover, our modeling approach offers additional advantages over the previously developed methods, including the possibility of the analysis of multiple technical replicates for samples from different biological conditions, with an assessment of the between-spectra and biological variability.

High-speed data reduction, feature detection, and MS/MS spectrum quality assessment of shotgun proteomics data sets using high-resolution mass spectrometry.

Advances in Fourier transform mass spectrometry have made the acquisition of high-resolution and accurate mass measurements routine on a chromatographic time scale. Here we report an algorithm, Hardklör, for the rapid and robust analysis of high-resolution mass spectra acquired in shotgun proteomics experiments. Our algorithm is demonstrated in the analysis of an Escherichia coli enriched membrane fraction. The mass spectrometry data of the respective peptides are acquired by microcapillary HPLC on an LTQ-orbitrap mass spectrometer with data-dependent acquisition of MS/MS spectra. Hardklör detects 211,272 total peptide isotope distributions over a 2-h analysis (75-min gradient) in only a small fraction of the time required to acquire the data. From these data there are 13,665 distinct, chromatographically persistent peptide isotope distributions. Hardklör is also used to assess the quality of the product ion spectra and finds that more than 11.2% of the MS/MS spectra are composed of fragment ions from multiple different molecular species. Additionally, a method is reported that enzymatically labels N-linked glycosylation sites on proteins, creating a unique isotope signature that can be detected with Hardklör. Using the protein invertase, Hardklör identifies 18O-labeled peptide isotope distributions of four glycosylation sites. The speed and robustness of the algorithm create a versatile tool that can be used in many different areas of mass spectrometry data analysis.

Stable isotope composition of organic compounds transported in the phloem of European beech--evaluation of different methods of phloem sap collection and assessment of gradients in carbon isotope composition during leaf-to-stem transport.

The analysis of stable isotope composition (delta13C, delta15N, delta18O) of phloem-transported organic matter is a useful tool for assessing short-term carbon and water balance of trees. A major constraint of the general application of this method to trees at natural field sites is that the collection of phloem sap with the "phloem bleeding" technique is restricted to particular species and plant parts. To overcome this restriction, we compared the contents (amino compounds and sugars) and isotope signatures (delta13C, delta15N, delta18O) of phloem sap directly obtained from incisions in the bark (bleeding technique) with phloem exudates where bark pieces were incubated in aqueous solutions (phloem exudation technique with and without chelating agents [EDTA, polyphosphate] in the initial sampling solution, which prevent blocking of sieve tubes). A comparable spectrum of amino compounds and sugars was detected using the different techniques. O, C, or N compounds in the initial sampling solution originating from the chelating agents always decreased precision of determination of the respective isotopic signatures, as indicated by higher standard deviation, and/or led to a significant difference of mean delta as compared to the phloem bleeding technique. Hence, depending on the element from which the ratio of heavy to light isotope is determined, compounds lacking C, N, and/or O should be used as chelating agents in the exudation solution. In applying the different techniques, delta13C of organic compounds transported in the phloem of the twig (exudation technique with polyphosphate as chelating agent) were compared with those in the phloem of the main stem (phloem bleeding technique) in order to assess possible differences in carbon isotope composition of phloem carbohydrates along the tree axis. In July, organic compounds in the stem phloem were significantly enriched in 13C by > 1.3 per thousand as compared to the twig phloem, whereas this effect was not observed in September. Correlation analysis between delta13C and stomatal conductance (Gs) revealed the gradient from the twigs to the stem observed in July may be attributed to temporal differences rather than to spatial differences in carbon isotope composition of sugars. As various authors have produced conflicting results regarding the enrichment/depletion of 13C in organic compounds in the leaf-to-stem transition, the different techniques presented in this paper can be used to provide further insight into fractionation processes associated with transport of C compounds from leaves to branches and down the main stem.