An in silico MS/MS library for automatic annotation of novel FAHFA lipids.

BackgroundA new lipid class named 'fatty acid esters of hydroxyl fatty acids' (FAHFA) was recently discovered in mammalian adipose tissue and in blood plasma and some FAHFAs were found to be associated with type 2 diabetes. To facilitate the automatic annotation of FAHFAs in biological specimens, a tandem mass spectra (MS/MS) library is needed. Due to the limitation of the commercial available standard compounds, we proposed building an in silico MS/MS library to extend the coverage of molecules.ResultsWe developed a computer-generated library with 3267 tandem mass spectra (MS/MS) for 1089 FAHFA species. FAHFA spectra were generated based on authentic standards with negative mode electrospray ionization and 10, 20, and 40 V collision induced dissociation at 4 spectra/s as used in in ultra-high performance liquid chromatography-QTOF mass spectrometry studies. However, positional information of the hydroxyl group is only obtained either at lower QTOF spectra acquisition rates of 1 spectrum/s or at the MS(3) level in ion trap instruments. Therefore, an additional set of 4290 fragment-rich MS/MS spectra was created to enable distinguishing positional FAHFA isomers. The library was generated based on ion fragmentations and ion intensities of FAHFA external reference standards, developing a heuristic model for fragmentation rules and extending these rules to large swaths of computer-generated structures of FAHFAs with varying chain lengths, degrees of unsaturation and hydroxyl group positions. Subsequently, we validated the new in silico library by discovering several new FAHFA species in egg yolk, showing that this library enables high-throughput screening of FAHFA lipids in various biological matrices.ConclusionsThe developed library and templates are freely available for commercial or noncommercial use at http://fiehnlab.ucdavis.edu/staff/yanma/fahfa-lipid-library. This in silico MS/MS library allows users to annotate FAHFAs from accurate mass tandem mass spectra in an easy and fast manner with NIST MS Search or PepSearch software. The developing template is provided for advanced users to modify the parameters and export customized libraries according to their instrument features. Graphical abstractExample of experimental and in silico MS/MS spectra for FAHFA lipids

Distinctive Patterns of Flavonoid Biosynthesis in Roots and Nodules of Datisca glomerata and Medicago spp. Revealed by Metabolomic and Gene Expression Profiles

Plants within the Nitrogen-fixing Clade (NFC) of Angiosperms form root nodule symbioses with nitrogen-fixing bacteria. Actinorhizal plants (in Cucurbitales, Fagales, Rosales) form symbioses with the actinobacteria Frankia while legumes (Fabales) form symbioses with proteobacterial rhizobia. Flavonoids, secondary metabolites of the phenylpropanoid pathway, have been shown to play major roles in legume root nodule symbioses: as signal molecules that in turn trigger rhizobial nodulation initiation signals and acting as polar auxin transport inhibitors, enabling a key step in nodule organogenesis. To explore a potentially broader role for flavonoids in root nodule symbioses across the NFC, we combined metabolomic and transcriptomic analyses of roots and nodules of the actinorhizal host Datisca glomerata and legumes of the genus Medicago. Patterns of biosynthetic pathways were inferred from flavonoid metabolite profiles and phenylpropanoid gene expression patterns in the two hosts to identify similarities and differences. Similar classes of flavonoids were represented in both hosts, and an increase in flavonoids generally in the nodules was observed, with differences in flavonoids prominent in each host. While both hosts produced derivatives of naringenin, the metabolite profile in D. glomerata indicated an emphasis on the pinocembrin biosynthetic pathway, and an abundance of flavonols with potential roles in symbiosis. Additionally, the gene expression profile indicated a decrease in expression in the lignin/monolignol pathway. In Medicago sativa, by contrast, isoflavonoids were highly abundant featuring more diverse and derived isoflavonoids than D. glomerata. Gene expression patterns supported these differences in metabolic pathways, especially evident in a difference in expression of cinnamic acid 4-hydroxylase (C4H), which was expressed at substantially lower levels in D. glomerata than in a Medicago truncatula transcriptome where it was highly expressed. C4H is a major rate-limiting step in phenylpropanoid biosynthesis that separates the pinocembrin pathway from the lignin/monolignol and naringenin-based flavonoid branches. Shikimate O-hydroxycinnamoyltransferase, the link between flavonoid biosynthesis and the lignin/monolignol pathway, was also expressed at much lower levels in D. glomerata than in M. truncatula. Our results indicate (a) a likely major role for flavonoids in actinorhizal nodules, and (b) differences in metabolic flux in flavonoid and phenylpropanoid biosynthesis between the different hosts in symbiosis

Combining Experimental and In Silico Methods for Comprehensive Compound Dereplication of Natural Products for Mass Spectrometry Based Metabolomics

Metabolomics is a rapidly growing field in “omics” research where metabolites are analyzed in biological systems. Over the past decade, mass spectrometry (MS) based metabolomics has been used for its superior analytical performance to reveal how these biological systems respond to genetic and environmental changes. MS is both sensitive and selective and is capable for providing comprehensive information for metabolic profiling by combining separation methods such as liquid chromatography (LC-MS) or gas chromatography (GC-MS). However, in untargeted metabolomics identification of small molecules is the bottleneck. In the research described here, I have combined both in silico and experimental methods for compound dereplication of natural products using MS-based metabolomics. Chapter 1 addresses the advancement of fragmentation and mass spectral trees used for unknown metabolite identification. Tools used for metabolite identification from the past 10 years are discussed, including algorithms, software, mass spectral libraries, and databases that implement fragmentation and mass spectral trees. Due to the inherent complexity of natural products in plants and microbes, unknown compound identification is increasingly difficult and limiting. Resolving this problem requires better computational tools and informative data such as those acquired by multi-stage mass spectrometry (MSn). MSn yields more fragmentation data and allows for more complex structural elucidation as needed for compounds with positional isomers. The limitation with using tandem mass spectrometry (MS/MS) only is that many ions are shared between positional isomers and full structural information is not available to elucidate an unknown metabolite. Fragmentation and mass spectral trees both describe the fragmentation processes of a metabolite and aid in fragmentation rule generation and substructure identification. The major difference between fragmentation and mass spectral trees is that fragmentation trees use elemental compositions to describe the fragmentation process and mass spectral trees or ion trees use precursor and product ion spectra from MSn mass spectral acquisition. As a result, there has been a large increase in efforts to develop MSn > 2 data and tools for both structure elucidation and spectral annotations with the use of fragmentation and mass spectral trees in recent years. Chapter 2 describes research and development of iTree, a MSn mass spectral tree library of plant natural products and its aid in compound identification of natural products. In metabolomics, mass spectral library searching is a standard method for compound identification, correctly known as compound dereplication. Mass spectral libraries are either freely or commercially available and can contain both experimental and in silico MS/MS reference spectra. The coverage of MSn > 2 reference spectra is much smaller in many of these MS/MS libraries and databases. To date the largest MSn > 2 libraries are HighChem and mzCloud, which also support mass spectral trees. The chemical coverage of such libraries and databases are very low in comparison to the number of known compounds. iTree was developed to expand the coverage of fragmentation spectra for natural products. iTree contains more than 2,000 natural products and more than 9,000 ion tree spectra annotated with in silico generated substructures from both Mass Frontier 7.0 and CFM-ID. iTree is freely available through MassBank of North America (MoNA), an open-access mass spectral database. As a result of the high number of natural products, and specifically flavonoid aglycones, previously published fragmentation rules were studied and validated. A new rule for flavanonols was proposed as a loss of –CCO to occur specifically for this class. In addition, iTree was used to profile secondary metabolites in the roots and nodules of the host plant Datisca glomerata. More than 100 natural products were identified by combining LC-MSn, high resolution LC-MS/MS, and ion tree analysis using iTree. Overall, iTree has shown to provide a method to facilitate metabolite identification for plant natural products. Although MSn > 2 data is more useful for complex structural elucidation, the predominant data used in untargeted metabolomics is MS/MS. For this reason, in silico tools that focus on the interpretation of MS and MS/MS spectra alone must be evaluated. In Chapters 3 through 5, I discuss how the Critical Assessment of Small Molecule Identification (CASMI) has allowed for such an evaluation by presenting unknown challenge data sets to the metabolomics community to evaluate the tools and methods they currently use for unknown compound identification. The results submitted by each user are compared and discussed to provide greater insight into how in silico tools can be further improved to aid in the advancement and accuracy of unknown compound identification methods. Chapter 3 focuses specifically on the performance of MS-FINDER, a software that uses MS and MS/MS spectra for structural elucidation of unknown compounds, presented in the CASMI 2016 Category 1. (Abstract shortened by ProQuest.

Using MS-FINDER for identifying 19 natural products in the CASMI 2016 contest

In its fourth year, the CASMI 2016 contest was organized to evaluate current chemical structure identification strategies for 19 natural products using high-resolution LC-MS and LC-MS/MS challenge datasets using automated methods with or without the combination of other tools. These natural products originate from plants, fungi, marine sponges, algae, or micro-algae. Every compound annotation workflow must start with determination of elemental compositions. Of these 19 challenges, one was excluded by the organizers after submission. For the remaining 18 challenges, three software programs were used. MS-FINDER version 1.62 was able to correctly identify 89% of the molecular formulas using an internal database that comprised of 13 metabolomics repositories with 45,181 formulas. SIRIUS correctly identified 61% compositions using PubChem formulas and Seven Golden Rules correctly identified 83% by using the Dictionary of Natural Products as a targeted database. Next, we performed structural dereplication for which we used the consensus formula from the three software programs. We submitted two solution sets for these challenges. In the first solution set, avaniya001, we only used the internal MS-FINDER functions for predicting and ranking structures, correctly identifying 53% of the structures as top-hit, 72% within the top-3 structures, and 78% within the top-10 hits. For our second set, avaniya002, we used both MS-FINDER predictions as well as MS/MS queries against the commercial NIST 14, METLIN, and the public MassBank of North America libraries. Here we correctly identified 78% of the structures as top-hit and 83% within the top-3 hits. Three challenge spectra remained unidentified in either of our submissions within the top-10 hits

Metabolomics Reveals the Molecular Mechanisms of Copper Induced Cucumber Leaf ( Cucumis sativus) Senescence.

Excess copper may disturb plant photosynthesis and induce leaf senescence. The underlying toxicity mechanism is not well understood. Here, 3-week-old cucumber plants were foliar exposed to different copper concentrations (10, 100, and 500 mg/L) for a final dose of 0.21, 2.1, and 10 mg/plant, using CuSO4 as the Cu ion source for 7 days, three times per day. Metabolomics quantified 149 primary and 79 secondary metabolites. A number of intermediates of the tricarboxylic acid (TCA) cycle were significantly down-regulated 1.4-2.4 fold, indicating a perturbed carbohydrate metabolism. Ascorbate and aldarate metabolism and shikimate-phenylpropanoid biosynthesis (antioxidant and defense related pathways) were perturbed by excess copper. These metabolic responses occur even at the lowest copper dose considered although no phenotype changes were observed at this dose. High copper dose resulted in a 2-fold increase in phytol, a degradation product of chlorophyll. Polyphenol metabolomics revealed that some flavonoids were down-regulated, while the nonflavonoid 4-hydroxycinnamic acid and trans-2-hydroxycinnamic acid were significantly up-regulated 4- and 26-fold compared to the control. This study enhances current understanding of copper toxicity to plants and demonstrates that metabolomics profiling provides a more comprehensive view of plant responses to stressors, which can be applied to other plant species and contaminants