Untargeted foodomics for authenticating the organic farming of water spinach (Ipomoea aquatica).

This study aimed to conduct a comprehensive analysis of the primary and secondary metabolites of water spinach (Ipomoea aquatica) using hydrophilic interaction liquid chromatography coupled with Orbitrap high-resolution mass spectrometry (HILIC-Orbitrap-HRMS). Certified samples from two cultivars, Green stem water spinach (G) and White stem water spinach (W) cultivated using organic and conventional farming methods, were collected from the Hong Kong market. Multivariate analysis was used to differentiate water spinach of different cultivars and farming methods. We identified 12 metabolites to distinguish between G and W, 26 metabolites to identify G from organic farming and 8 metabolites to identify W from organic farming. Then, two metabolites, isorhamnetin and jasmonic acid, have been proposed to serve as biomarkers for organic farming (in both G and W). Our foodomics findings provide useful tools for improving the crop performance of water spinach under abiotic/biotic stressesand authentication of organic produce.

Insights into serum metabolic biomarkers for early detection of incident diabetic kidney disease in Chinese patients with type 2 diabetes by random forest.

Diabetic kidney disease (DKD) is a leading cause of end-stage renal disease (ESRD) worldwide. Early detection is critical for the risk stratification and early intervention of progressive DKD. Serum creatinine (sCr) and urine output are used to assess kidney function, but these markers are limited by their delayed changes following kidney pathology, and lacking of both sensitivity and accuracy. Hence, it is essential to illustrate potential diagnostic indicators to enhance the precise prediction of early DKD. A total of 194 Chinese individuals include 30 healthy participants (Stage 0) and 164 incidents with type 2 diabetes (T2D) spanning from DKD's Stage 1a to 4 were recruited and their serums were subjected for untargeted metabolomic analysis. Random forest (RF), a machine learning approach, together with univariate linear regression (ULR) and multivariate linear regression (MvLR) analysis were applied to characterize the features of untargeted metabolites of DKD patients and to identify candidate DKD biomarkers. Our results indicate that 2-(α-D-mannopyranosyl)-L-tryptophan (ADT), succinyladenosine (SAdo), pseudouridine and N,N,N-trimethyl-L-alanyl-L-proline betaine (L-L-TMAP) were associated with the development of DKD, in particular, the latter three that were significantly elevated in Stage 2-4 T2D incidents. Each of the four metabolites in combination with sCr achieves better performance than sCr alone with area under the receiver operating characteristic curve (AUC) of 0.81-0.91 in predicting DKD stages. An average of 3.9 years follow-up study of another cohort including 106 Stage 2-3 patients suggested that "urinary albumin-to-creatinine ratio (UACR) + ADT + SAdo" can be utilized for better prognosis evaluation of early DKD (average AUC = 0.9502) than UACR without sexual difference.

Mass spectrometry-based untargeted metabolomics approach for differentiation of beef of different geographic origins.

Beef is a common staple food in many countries, and there is a growing concern over misinformation of beef products, such as false claims of origin, species and production methods. In this study, we used a mass spectrometry-based metabolomics approach to study the metabolite profiles of beef samples purchased from local retailers in Hong Kong. Using multivariate analysis, beef samples from different a) geographical origins, namely the United States (US), Japan and Australia, and b) feeding regimes could be differentiated. We identified twenty-four metabolites to distinguish beef samples from different countries, ten metabolites to identify Angus beef samples from others and seven metabolites to discriminate Australian beef produced by the organic farming from that produced using other farming modes. Based on results of this study, it is concluded that metabolomics provides an efficient strategy for tracing and authenticating beef products to ensure their quality and to protect consumer rights.

Qualitative and quantitative analysis of chemical constituents of Centipeda minima by HPLC-QTOF-MS & HPLC-DAD.

A high performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (HPLC-QTOF-MS) method in both positive and negative ion modes was established to investigate the major constituents in the ethanolic extract of Centipeda minima (EBSC). Twelve common components including flavones and their glycosides, phenolic and polyphenolic acids, and sesquiterpene lactone were identified in ten batches of samples based on comparison with the retention time and accurate mass of external standards (mass accuracy within 3ppm) or the fragmentation patterns of tandem MS. Meanwhile, a simple, accurate and reliable HPLC-DAD method was also developed to determine the content of 10 chemical markers simultaneously. Results obtained from method validations including linearity, accuracy and precision showed that this new method is reliable and robust. Isochlorogenic acid A and brevilin A were found to be the most abundant in the ethanol extract of EBSC and could be served as markers for quality control of EBSC.

Prediction of peptide fragment ion mass spectra by data mining techniques.

Accurate prediction of peptide fragment ion mass spectra is one of the critical factors to guarantee confident peptide identification by protein sequence database search in bottom-up proteomics. In an attempt to accurately and comprehensively predict this type of mass spectra, a framework named MS(2)PBPI is proposed. MS(2)PBPI first extracts fragment ions from large-scale MS/MS spectra data sets according to the peptide fragmentation pathways and uses binary trees to divide the obtained bulky data into tens to more than 1000 regions. For each adequate region, stochastic gradient boosting tree regression model is constructed. By constructing hundreds of these models, MS(2)PBPI is able to predict MS/MS spectra for unmodified and modified peptides with reasonable accuracy. Moreover, high consistency between predicted and experimental MS/MS spectra derived from different ion trap instruments with low and high resolving power is achieved. MS(2)PBPI outperforms existing algorithms MassAnalyzer and PeptideART.

Investigation of scrambled ions in tandem mass spectra, part 2. On the influence of the ions on peptide identification.

A comprehensive investigation was performed to understand the influence of sequence scrambling in peptide ions on peptide identification results. To achieve this, four tandem mass spectrometry datasets with scrambled ions included and with them excluded were analyzed by Crux, X!Tandem, SpectraST, Lutefisk, and PepNovo. While the different algorithms differed in their performance, an increase in the number of correctly identified peptides was generally observed when removing scrambled ions, with the exception of the SpectraST algorithm. However, the variation of the match scores upon removal was unpredictable. Following these investigations, an interpretation was given on how the scrambled ions affect peptide identification. Lastly, a simulated theoretical mass spectral library derived from the NIST peptide Libraries was constructed and searched by SpectraST to study whether scrambled ions in predicted mass spectra could affect peptide identification. Consistent with the peptide library search results, no significant variations for dot product scores as well as peptide identification results were observed when these ions were included in the theoretical MS/MS spectra. From the five adopted algorithms, the SpectraST and Crux provided the most robust results, whereas X!Tandem, PepNovo, and Lutefisk were sensitive to the existence of the scrambled ions, especially the latter two de novo sequencing algorithms.

Investigation of scrambled ions in tandem mass spectra. Part 1. Statistical characterization.

Scrambled ions have become the focus of recent investigations of peptide fragmentation. Here, an investigation of more than 390,000 high quality CID mass spectra is presented to explore the extent of scrambled ions in mass spectra and the possible fragmentation rules during scramble reactions. For the former, scrambled ions generally make up more than 10 % of mass spectra in number, although the abundances are less than 0.1 of the base peak. For the latter, relatively preferential re-opening sites were found for aliphatic residues Ala, Ile, Leu, and other residues such as Met, Gln, Ser, Phe, and Thr, whereas disfavored sites were found for basic residues Arg, Lys, and His, and Trp for both scrambled b and a ions. Similar preferential order in re-opening reaction was found in the reaction of losing internal residues when cleavage occurs at C-terminal side of 20 residues. However, when cleavage occurs at N-terminal side, Glu, Phe, and Trp become the most preferential sites. These results provide a deep insight into cleavage rules during scramble reactions for prediction of peptide mass spectra. Also, an additional investigation of whether scrambled ions could help discriminate false identifications from correct identifications was performed. Probing the number fraction of scrambled ions in falsely and correctly interpreted spectra and analyzing the correlation between scrambled ions and SEQUEST scores XCorr and Sp showed scrambled ions could at some extent help improve the discrimination in singly charged identifications, whereas no improvement was found for multiply charged results.