Plasma levels of platinum-induced fatty acid [16:4n-3] do not affect response to platinum-based chemotherapy: A pilot study in non-small cell lung cancer patients.

BACKGROUND & AIMS: Pre-clinical studies suggest that 16:4(n-3) in purified form or as a component of fish oil might induce platinum-based chemotherapy resistance. Our aim was to determine plasma total and free 16:4(n-3) before and during platinum-based chemotherapy in non-small cell lung cancer (NSCLC) patients supplemented with fish oil or provided standard care, and to explore relationships between plasma 16:4(n-3) levels and tumor response to treatment. METHODS: In a retrospective, secondary data analysis of a prior clinical trial, plasma from patients with NSCLC (n = 21) who underwent platinum-based chemotherapy and were assigned to 2.2 g/day of eicosapentaenoic (EPA) plus 1.1 g DHA/day as fish oil (FO; n = 12) or received no intervention (standard care; SC; n = 9). Plasma 16:4(n-3) was quantified as free and esterified (total) fatty acid using HPLC-MS/MS. Plasma 16:4(n-3) levels were evaluated over time in relation to fish oil supplementation and response to platinum-based therapy, and compared with a group of healthy subjects (REF; n = 11). RESULTS: Plasma 16:4(n-3) was detected in all samples. The percentage change/day in plasma esterified (total) 16:4(n-3) was higher for FO versus SC group (2.7 versus -1.8%/d, U = 20, p = 0.02), but change in plasma free 16:4(n-3) was not different between FO and SC. Median plasma free and esterified 16:4(n-3) were similar between responders and non-responders to platinum-based chemotherapy. Total and free plasma 16:4(n-3) fatty acids were similar between NSCLC patients and REF (NSCLC vs REF: total 16:4(n-3): 122.9 vs. 95.2 nM and free 16:4(n-3) 23.9 vs. 27.6 nM). CONCLUSIONS: This first of its kind study that evaluated plasma 16:4(n-3) in NSCLC patients showed that 16:4 (n-3) was elevated during FO supplementation, independent of fish oil supplementation or platinum-based chemotherapy.

PRESS timings for resolving 13 C4 -glutamate 1 H signal at 9.4 T: Demonstration in rat with uniformly labelled 13 C-glucose.

MRS of 13 C4 -labelled glutamate (13 C4 -Glu) during an infusion of a carbon-13 (13 C)-labelled substrate, such as uniformly labelled glucose ([U-13 C6 ]-Glc), provides a measure of Glc metabolism. The presented work provides a single-shot indirect 13 C detection technique to quantify the approximately 2.51 ppm 13 C4 -Glu satellite proton (1 H) peak at 9.4 T. The methodology is an optimized point-resolved spectroscopy (PRESS) sequence that minimizes signal contamination from the strongly coupled protons of N-acetylaspartate (NAA), which resonate at approximately 2.49 ppm. J-coupling evolution of protons was characterized numerically and verified experimentally. A (TE1 , TE2 ) combination of (20 ms, 106 ms) was found to be suitable for minimizing NAA signal in the 2.51 ppm 1 H 13 C4 -Glu spectral region, while retaining the 13 C4 -Glu 1 H satellite peak. The efficacy of the technique was verified on phantom solutions and on two rat brains in vivo during an infusion of [U-13 C6 ]-Glc. LCModel was employed for analysis of the in vivo spectra to quantify the 2.51 ppm 1 H 13 C4 -Glu signal to obtain Glu C4 fractional enrichment time courses during the infusions. Cramér-Rao lower bounds of about 8% were obtained for the 2.51 ppm 13 C4 -Glu 1 H satellite peak with the optimal TE combination.

MyCompoundID: using an evidence-based metabolome library for metabolite identification.

Identification of unknown metabolites is a major challenge in metabolomics. Without the identities of the metabolites, the metabolome data generated from a biological sample cannot be readily linked with the proteomic and genomic information for studies in systems biology and medicine. We have developed a web-based metabolite identification tool ( http://www.mycompoundid.org ) that allows searching and interpreting mass spectrometry (MS) data against a newly constructed metabolome library composed of 8,021 known human endogenous metabolites and their predicted metabolic products (375,809 compounds from one metabolic reaction and 10,583,901 from two reactions). As an example, in the analysis of a simple extract of human urine or plasma and the whole human urine by liquid chromatography-mass spectrometry and MS/MS, we are able to identify at least two times more metabolites in these samples than by using a standard human metabolome library. In addition, it is shown that the evidence-based metabolome library (EML) provides a much superior performance in identifying putative metabolites from a human urine sample, compared to the use of the ChemPub and KEGG libraries.

A method for comprehensive analysis of urinary acylglycines by using ultra-performance liquid chromatography quadrupole linear ion trap mass spectrometry.

Acylglycines are an important class of metabolites that have been used in the diagnosis of several inborn errors of metabolism (IEM). However, current analytical methods detect only a few acylglycines. There is a need to profile these metabolites in a comprehensive manner for studying their functions and improving their diagnostic values for different IEM and potentially other diseases. We describe a sensitive method that combines the chromatographic resolving power of ultra-performance liquid chromatography (UPLC) to separate closely related metabolites including isomers with tandem mass spectrometry (MS/MS). Acylglycines were extracted from urine using an anion exchange solid-phase extraction (SPE) cartridge. After UPLC separation, the acylglycines were detected on a hybrid triple quadrupole linear ion trap mass spectrometer. A set of standards were used for the development of an optimal MS acquisition method. Several acquisition modes using information derived from collision-induced dissociation breakdown curves were used to detect acylglycines. Using this method, 18 acylglycines were detected in the urine of healthy individuals and confirmed using standards, while 47 additional acylglycines were detected and tentatively identified, based on their retention and fragmentation pattern. Among the 65 acylglycines detected, only 18 of them have been previously reported in biofluids of healthy individuals. These results will be deposited in a public human metabolome database. This example illustrates that by developing a method tailored to the analysis of a class of metabolites sharing similar structural moieties, we can potentially identify many more new metabolites, thereby expanding the overall metabolome coverage.