Rapid Screening of Ellagitannins in Natural Sources via Targeted Reporter Ion Triggered Tandem Mass Spectrometry.

Complex biomolecules present in their natural sources have been difficult to analyze using traditional analytical approaches. Ultrahigh-performance liquid chromatography (UHPLC-MS/MS) methods have the potential to enhance the discovery of a less well characterized and challenging class of biomolecules in plants, the ellagitannins. We present an approach that allows for the screening of ellagitannins by employing higher energy collision dissociation (HCD) to generate reporter ions for classification and collision-induced dissociation (CID) to generate unique fragmentation spectra for isomeric variants of previously unreported species. Ellagitannin anions efficiently form three characteristic reporter ions after HCD fragmentation that allows for the classification of unknown precursors that we call targeted reporter ion triggering (TRT). We demonstrate how a tandem HCD-CID experiment might be used to screen natural sources using UHPLC-MS/MS by application of 22 method conditions from which an optimized data-dependent acquisition (DDA) emerged. The method was verified not to yield false-positive results in complex plant matrices. We were able to identify 154 non-isomeric ellagitannins from strawberry leaves, which is 17 times higher than previously reported in the same matrix. The systematic inclusion of CID spectra for isomers of each species classified as an ellagitannin has never been possible before the development of this approach.

Detection of hepatocellular carcinoma in hepatitis C patients: biomarker discovery by LC-MS.

Hepatocellular carcinoma (HCC) accounts for most cases of liver cancer worldwide; contraction of hepatitis C (HCV) is considered a major risk factor for liver cancer even when individuals have not developed formal cirrhosis. Global, untargeted metabolic profiling methods were applied to serum samples from patients with either HCV alone or HCC (with underlying HCV). The main objective of the study was to identify metabolite based biomarkers associated with cancer risk, with the long term goal of ultimately improving early detection and prognosis. Serum global metabolite profiles from patients with HCC (n=37) and HCV (n=21) were obtained using high performance liquid chromatography-mass spectrometry (HPLC-MS) methods. The selection of statistically significant metabolites for partial least-squares discriminant analysis (PLS-DA) model creation based on biological and statistical significance was contrasted to that of a traditional approach utilizing p-values alone. A PLS-DA model created using the former approach resulted in a model with 92% sensitivity, 95% specificity, and an AUROC of 0.93. A series of PLS-DA models iteratively utilizing three to seven metabolites that were altered significantly (p<0.05) and sufficiently (FC≤0.7 or FC≥1.3) showed good performance using p-values alone; the best of these PLS-DA models was capable of generating 73% sensitivity, 95% specificity, and an AUROC of 0.92. Metabolic profiles derived from LC-MS readily distinguish patients with HCC and HCV from those with HCV only. Differences in the metabolic profiles between high-risk individuals and HCC indicate the possibility of identifying the early development of liver cancer in at risk patients. The use of biological significance as a selection process prior to PLS-DA modeling may offer improved probabilities for translation of newly discovered biomarkers to clinical application.

Metabolomics approach for predicting response to neoadjuvant chemotherapy for breast cancer.

Breast cancer is a clinically heterogeneous disease, which necessitates a variety of treatments and leads to different outcomes. As an example, only some women will benefit from chemotherapy. Identifying patients who will respond to chemotherapy and thereby improve their long-term survival has important implications to treatment protocols and outcomes, while identifying non responders may enable these patients to avail themselves of other investigational approaches or other potentially effective treatments. In this study, serum metabolite profiling was performed to identify potential biomarker candidates that can predict response to neoadjuvant chemotherapy for breast cancer. Metabolic profiles of serum from patients with complete (n = 8), partial (n = 14) and no response (n = 6) to chemotherapy were studied using a combination of nuclear magnetic resonance (NMR) spectroscopy, liquid chromatography-mass spectrometry (LC-MS) and statistical analysis methods. The concentrations of four metabolites, three (threonine, isoleucine, glutamine) from NMR and one (linolenic acid) from LC-MS were significantly different when comparing response to chemotherapy. A prediction model developed by combining NMR and MS derived metabolites correctly identified 80% of the patients whose tumors did not show complete response to chemotherapy. These results show promise for larger studies that could result in more personalized treatment protocols for breast cancer patients.

Esophageal cancer metabolite biomarkers detected by LC-MS and NMR methods.

BACKGROUND: Esophageal adenocarcinoma (EAC) is a rarely curable disease and is rapidly rising worldwide in incidence. Barret's esophagus (BE) and high-grade dysplasia (HGD) are considered major risk factors for invasive adenocarcinoma. In the current study, unbiased global metabolic profiling methods were applied to serum samples from patients with EAC, BE and HGD, and healthy individuals, in order to identify metabolite based biomarkers associated with the early stages of EAC with the goal of improving prognostication. METHODOLOGY/PRINCIPAL FINDINGS: Serum metabolite profiles from patients with EAC (n = 67), BE (n = 3), HGD (n = 9) and healthy volunteers (n = 34) were obtained using high performance liquid chromatography-mass spectrometry (LC-MS) methods. Twelve metabolites differed significantly (p<0.05) between EAC patients and healthy controls. A partial least-squares discriminant analysis (PLS-DA) model had good accuracy with the area under the receiver operative characteristic curve (AUROC) of 0.82. However, when the results of LC-MS were combined with 8 metabolites detected by nuclear magnetic resonance (NMR) in a previous study, the combination of NMR and MS detected metabolites provided a much superior performance, with AUROC = 0.95. Further, mean values of 12 of these metabolites varied consistently from healthy controls to the high-risk individuals (BE and HGD patients) and EAC subjects. Altered metabolic pathways including a number of amino acid pathways and energy metabolism were identified based on altered levels of numerous metabolites. CONCLUSIONS/SIGNIFICANCE: Metabolic profiles derived from the combination of LC-MS and NMR methods readily distinguish EAC patients and potentially promise important routes to understanding the carcinogenesis and detecting the cancer. Differences in the metabolic profiles between high-risk individuals and the EAC indicate the possibility of identifying the patients at risk much earlier to the development of the cancer.

Adjacent pulsed nanoelectrospray ionization emitters for the alternating generation of ions of opposite polarity.

An approach that allows for adjacent closely spaced nanoelectrospray ionization (nESI) emitters to be pulsed alternately to generate ions of opposite polarity for transmission through a common interface is described. The potential difference between two or more nESI emitters in close proximity is minimized by applying the same polarity to both emitters at any given point in time but with the magnitude of only the active emitter's potential being sufficiently high to sustain a stable spray. The reduced difference in potential between emitters allows the distance between emitters to be decreased to within a few millimeters so that compromises imposed by the use of multiple emitters for the generation of ions from distinct solutions using a common atmosphere interface are minimized.

Protein identification via ion-trap collision-induced dissociation and examination of low-mass product ions.

A whole-protein tandem mass spectrometry approach for protein identification based on precursor ion charge state concentration via ion/ion reactions, ion-trap collisional activation, ion/ion proton-transfer reactions involving the product ions, and mass analysis over a narrow m/z range (up to m/z 2000) is described and evaluated. The experiments were carried out with a commercially available electrospray ion-trap instrument that has been modified to allow for ion/ion reactions. Reaction conditions and the approach to searching protein databases were developed with the assumption that the resolving power of the mass analyzer is insufficient to distinguish charge states on the basis of the isotope spacings. Ions derived from several charge states of cytochrome c, myoglobin, ribonuclease A, and ubiquitin were used to evaluate the approach for protein identification and to develop a two-step procedure to database searching to optimize specificity. The approach developed with the model proteins was then applied to whole cell lysate fractions of Saccharomyces cerevisiae. The results are illustrated with examples of assignments made for three a priori unknown proteins, each selected randomly from a lysate fraction. Two of the three proteins were assigned to species present in the database, whereas one did not match well any database entry. The combination of the mass measurement and the product ion masses suggested the possibility for the oxidation of two methionine residues of a protein in the database. The examples show that this limited whole-protein characterization approach can provide insights that might otherwise be lacking with approaches based on complete enzymatic digestion.