LOBSTAHS: An Adduct-Based Lipidomics Strategy for Discovery and Identification of Oxidative Stress BiomarkersClick to copy article linkArticle link copied!
Abstract
Discovery and identification of molecular biomarkers in large LC/MS data sets requires significant automation without loss of accuracy in the compound screening and annotation process. Here, we describe a lipidomics workflow and open-source software package for high-throughput annotation and putative identification of lipid, oxidized lipid, and oxylipin biomarkers in high-mass-accuracy HPLC-MS data. Lipid and oxylipin biomarker screening through adduct hierarchy sequences, or LOBSTAHS, uses orthogonal screening criteria based on adduct ion formation patterns and other properties to identify thousands of compounds while providing the user with a confidence score for each assignment. Assignments are made from one of two customizable databases; the default databases contain 14 068 unique entries. To demonstrate the software’s functionality, we screened more than 340 000 mass spectral features from an experiment in which hydrogen peroxide was used to induce oxidative stress in the marine diatom Phaeodactylum tricornutum. LOBSTAHS putatively identified 1969 unique parent compounds in 21 869 features that survived the multistage screening process. While P. tricornutum maintained more than 92% of its core lipidome under oxidative stress, patterns in biomarker distribution and abundance indicated remodeling was both subtle and pervasive. Treatment with 150 μM H2O2 promoted statistically significant carbon-chain elongation across lipid classes, with the strongest elongation accompanying oxidation in moieties of monogalactosyldiacylglycerol, a lipid typically localized to the chloroplast. Oxidative stress also induced a pronounced reallocation of lipidome peak area to triacylglycerols. LOBSTAHS can be used with environmental or experimental data from a variety of systems and is freely available at https://github.com/vanmooylipidomics/LOBSTAHS.
Theory and Design of Software and Databases
Design and Scope of Lipid-Oxylipin Databases
Database Generation
Determination of Relative Abundances of Adduct Ions for Inclusion in Databases
Lipidomics Workflow Based on xcms, CAMERA, and LOBSTAHS
Scheme 1
Scheme aWe automate several functions of the ProteoWizard msConvert tool. (43)
Scheme b,cxcms (36-38) was chosen for its command-line features and because it permits follow-on use of the R package CAMERA (39) to identify isotopes.
Scheme dIPO (54) can be used to optimize the values of parameters for some xcms and CAMERA functions.
Scheme eMultiple assignments will likely exist for many peakgroups in a typical data set.
Scheme fThis criterion may be useful when the subject data set contains lipids of exclusively eukaryotic origin.
Scheme gIn the case of C2a, the adduct ion hierarchy for the parent compound is completely satisfied; i.e., the pseudospectrum contains peakgroups representing every adduct ion of the compound of greater theoretical abundance than the least abundant adduct ion present. In the case of C2b, the adduct ion of greatest theoretical abundance and some lesser adduct ion is present, but adduct ions of intermediate abundance are not observed.
Scheme hBoth outcomes may apply simultaneously at this decision point if the data set contains isobars and isomers of the assignment.
Scheme iAnnotation codes (in bold) may be applied as indicated; these are designed to assist the user in evaluating assignment confidence during subsequent data analysis.
Database Assignments and Progressive Screening in LOBSTAHS Using Orthogonal Criteria
Experimental Section
Model Data Set Used To Demonstrate the Workflow
Sample Collection and Extraction
HPLC-ESI-MS Analysis
lipid class | origin of standard | moieties present in standarda | dominant positive mode adduct ion | ion exact m/z | observed m/zb | rel. mass uncertainty (ppm)c | correct LOBSTAHS ID? | confidence in assignment after adduct hierarchy screeningd | structural isomers or isobars present after screening? |
---|---|---|---|---|---|---|---|---|---|
MGDG | natural | 34:0 | [M + NH4]+ | 776.6246 | 776.6248 | 0.2 | yes | high | no |
36:0 | [M + NH4]+ | 804.6559 | 804.6561 | 0.3 | yes | high | no | ||
DNP-PE | synthetic | 32:0 | [M + NH4]+ | 875.5505 | 875.5507 | 0.2 | yes | high | no |
SQDG | natural | 34:3 | [M + NH4]+ | 834.5396 | 834.5398 | 0.2 | yes | high | no |
34:2 | [M + NH4]+ | 836.5552 | 836.5554 | 0.2 | yes | high | no | ||
PG | synthetic | 32:0 | [M + NH4]+ | 740.5436 | 740.5438 | 0.3 | yes | high | no |
PE | synthetic | 32:0 | [M + H]+ | 692.5225 | 692.5227 | 0.3 | yes | high | no |
PC | synthetic | 32:0 | [M + H]+ | 734.5694 | 734.5696 | 0.2 | yes | high | no |
DGDG | natural | 34:2 | [M + NH4]+ | 934.6462 | 934.6463 | 0.1 | yes | high | yes |
36:4 | [M + NH4]+ | 958.6462 | 958.6463 | 0.1 | yes | high | yes | ||
mean | 0.2 |
Multiple moieties were present in glycolipid standards purified from natural samples; only predominant moieties are shown.
Mean observed m/z ratio in 5 independent samples.
See the following equation:
“High confidence”: Assignment fully satisfied all adduct hierarchy rules and other screening criteria.
Analysis of P. tricornutum Data Using LOBSTAHS
Results and Discussion
Screening and Annotation of P. tricornutum Data in LOBSTAHS
no. present in data set | ||||
---|---|---|---|---|
operation(s) applied | peaks | peak groups | database assignmentsa | unique parent compounds |
xcms and CAMERA | ||||
initial feature detection; preprocessing | 340 991 | 18 314 | ||
LOBSTAHS | ||||
eliminate secondary isotope peaks | 163 938 | 12 146 | ||
apply initial compound assignments from database | 67 862 | 5077 | 15 929 | 14 076 |
apply RT screening criteria | 60 070 | 4451 | 13 504 | 11 779 |
exclude IP-DAG/TAG with odd total no. of acyl C atoms | 52 337 | 3871 | 7458 | 6283 |
adduct ion hierarchy screening | 21 869 | 1595 | 2056b | 1969 |
Figure reflects all assignments from database, including photosynthetic pigments.
1163, or 57%, of these had no competing assignments such as functional structural isomers or isobars; these 1163 assignments represented 990 unique parent compounds.
Figure 1
Figure 1. (a) All IPL, ox-IPL, and TAG identified in the P. tricornutum data set with high confidence (N = 1039; figure excludes pigments). (b) Distribution by lipid class of high-confidence assignments made in the 0 and 150 μM H2O2 treatments at 24 h (N = 894 and N = 848, respectively). Ellipse size in (b) reflects the number of compounds identified within each class and treatment. The assignments presented in (a) and (b) fully satisfied the LOBSTAHS adduct hierarchy screening criteria (i.e., annotated “C1” or “C2a” according to the logic in Scheme 1) and had no competing assignments, such as possible structural isomers, identified in the data set. Excluded are those compounds having an odd total number of acyl carbon atoms. aGeneral direction of movement within m/z versus RT plot, for a given lipid class and oxidation state. The direction of movement that results from addition or removal of additional oxygen atom(s) varies by lipid class. bNot to scale.
Identification and Annotation of Isomers and Isobars
Annotation of Potential Regioisomers
Evaluation of Screening and Identification Performance Using Two Methods
Resilience of Core P. tricornutum Lipidome under Oxidative Stress
Figure 2
Figure 2. Remodeling of the Phaeodactylum tricornutum lipidome after 24 h, as visualized from data analyzed with LOBSTAHS. (a) Heatmap showing relative abundances across two treatments (0 and 150 μM H2O2) of all IPL, ox-IPL, and TAG identified with high confidence. Each row (N = 896) represents a different compound identified from the database; Figure S11 contains an expanded version of the plot that includes labels for each individual compound. (b) Heatmap detail, showing changes in the most abundant (N = 40) moieties of monogalactosyldiacylglycerol (MGDG), a lipid typically localized to the chloroplast. (c) Fraction of total peak area identified as triacylglycerol (TAG) at three time points during the experiment. Error bars are ± SD of two replicates. In (a) and (b), shading shows the relative abundance of each compound as a fold difference of the mean peak area observed in that treatment from the mean peak area of the compound observed across all treatments. Dendrogram clustering and group definitions were determined by similarity profile analysis. (60) The numbers and identities of the components assigned to each group in (a) are given in Table S5 and Figure S11. Solid black lines in the dendrogram indicate branching that was statistically significant (P ≤ 0.01).
Differences in Degree of Remodeling between Lipid Classes and Functional Groupings
Fatty Acid Chain Elongation Is an Apparent Response to Oxidative Stress in the Chloroplast
Significant Enrichment Observed in TAG
Conclusions
Supporting Information
The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.analchem.6b01260.
Methodological details, instructions for obtaining all software and data, supplementary discussion, supplementary data figures, supplementary data tables, and a supplementary chart showing examples of the types of isomers that can be identified with LOBSTAHS (PDF)
Terms & Conditions
Most electronic Supporting Information files are available without a subscription to ACS Web Editions. Such files may be downloaded by article for research use (if there is a public use license linked to the relevant article, that license may permit other uses). Permission may be obtained from ACS for other uses through requests via the RightsLink permission system: http://pubs.acs.org/page/copyright/permissions.html.
Acknowledgment
We thank Elizabeth Kujawinski, Winn Johnson, and Krista Longnecker for discussions on the analysis of metabolomics datasets and assistance with xcms; Marian Carlson and Matthew Johnson for discussions on the effects of oxidative stress in microorganisms; Eugene Melamud for answering our many questions about MAVEN; Assaf Vardi and Daniella Schatz for providing the P. tricornutum cell cultures. Liz Kujawinski provided thoughtful feedback on an earlier version of the manuscript. Finally, we thank two anonymous reviewers for critical comments that improved the manuscript significantly. This research was supported by the Gordon and Betty Moore Foundation through Grant GBMF3301 to B.A.S.V.M. This research was also funded in part by a grant to B.A.S.V.M. from the Simons Foundation and is a contribution of the Simons Collaboration on Ocean Processes and Ecology (SCOPE). J.R.C. acknowledges support from a U.S. Environmental Protection Agency (EPA) STAR Graduate Fellowship (Fellowship Assistance Agreement No. FP-91744301-0).
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- 34Ni, Z.; Milic, I.; Fedorova, M. Anal. Bioanal. Chem. 2015, 407, 5161– 5173 DOI: 10.1007/s00216-015-8536-2Google Scholar34Identification of carbonylated lipids from different phospholipid classes by shotgun and LC-MS lipidomicsNi, Zhixu; Milic, Ivana; Fedorova, MariaAnalytical and Bioanalytical Chemistry (2015), 407 (17), 5161-5173CODEN: ABCNBP; ISSN:1618-2642. (Springer)Oxidized lipids play a significant role in the pathogenesis of numerous oxidative stress-related human disorders, such as atherosclerosis, obesity, inflammation, and autoimmune diseases. Lipid peroxidn., induced by reactive oxygen and nitrogen species, yields a high variety of modified lipids. Among them, carbonylated lipid peroxidn. products (oxoLPP), formed by oxidn. of the fatty acid moiety yielding aldehydes or ketones (carbonyl groups), are electrophilic compds. that are able to modify nucleophilic substrates like proteins, nucleic acid, and aminophospholipids. Some carbonylated phosphatidylcholines possess pro-inflammatory activities. However, little is known about oxoLPP derived from other phospholipid (PL) classes. Here, the authors present a new anal. strategy based on the mass spectrometry (MS) of PL-oxoLPP derivatized with 7-(diethylamino)coumarin-3-carbohydrazide (CHH). Shotgun MS revealed many oxoLPP derived from in vitro oxidized glycerophosphatidylglycerols (PG, 31), glycerophosphatidylcholine (PC, 23), glycerophosphatidylethanolamine (PE, 34), glycerophosphatidylserines (PS, 7), glycerophosphatidic acids (PA, 17), and phosphatidylinositiolphosphates (PIP, 6) vesicles. This data were used to optimize LipidXplorer-assisted identification, and a python-based post-processing script was developed to increase both throughput and accuracy. When applied to full lipid exts. from rat primary cardiomyocytes treated with peroxynitrite donor SIN-1, ten PL-bound oxoLPP were unambiguously identified by LC-MS, including two PC-, two PE-, one PG-, two PS-, and three PA-derived species. Some of the known carbonylated PC were detected, while most PL-oxoLPP were shown for the first time. [Figure not available: see fulltext.].
- 35R Core Team. R Foundation for Statistical Computing: Vienna, Austria, 2015.Google ScholarThere is no corresponding record for this reference.
- 36Smith, C. A.; Want, E. J.; O’Maille, G.; Abagyan, R.; Siuzdak, G. Anal. Chem. 2006, 78, 779– 787 DOI: 10.1021/ac051437yGoogle Scholar36XCMS: Processing Mass Spectrometry Data for Metabolite Profiling Using Nonlinear Peak Alignment, Matching, and IdentificationSmith, Colin A.; Want, Elizabeth J.; O'Maille, Grace; Abagyan, Ruben; Siuzdak, GaryAnalytical Chemistry (2006), 78 (3), 779-787CODEN: ANCHAM; ISSN:0003-2700. (American Chemical Society)Metabolite profiling in biomarker discovery, enzyme substrate assignment, drug activity/specificity detn., and basic metabolic research requires new data preprocessing approaches to correlate specific metabolites to their biol. origin. Here we introduce an LC/MS-based data anal. approach, XCMS, which incorporates novel nonlinear retention time alignment, matched filtration, peak detection, and peak matching. Without using internal stds., the method dynamically identifies hundreds of endogenous metabolites for use as stds., calcg. a nonlinear retention time correction profile for each sample. Following retention time correction, the relative metabolite ion intensities are directly compared to identify changes in specific endogenous metabolites, such as potential biomarkers. The software is demonstrated using data sets from a previously reported enzyme knockout study and a large-scale study of plasma samples. XCMS is freely available under an open-source license at http://metlin.scripps.edu/download/.
- 37Tautenhahn, R.; Boettcher, C.; Neumann, S. BMC Bioinf. 2008, 9, 504 DOI: 10.1186/1471-2105-9-504Google Scholar37Highly sensitive feature detection for high resolution LC/MSTautenhahn Ralf; Bottcher Christoph; Neumann SteffenBMC bioinformatics (2008), 9 (), 504 ISSN:.BACKGROUND: Liquid chromatography coupled to mass spectrometry (LC/MS) is an important analytical technology for e.g. metabolomics experiments. Determining the boundaries, centres and intensities of the two-dimensional signals in the LC/MS raw data is called feature detection. For the subsequent analysis of complex samples such as plant extracts, which may contain hundreds of compounds, corresponding to thousands of features -- a reliable feature detection is mandatory. RESULTS: We developed a new feature detection algorithm centWave for high-resolution LC/MS data sets, which collects regions of interest (partial mass traces) in the raw-data, and applies continuous wavelet transformation and optionally Gauss-fitting in the chromatographic domain. We evaluated our feature detection algorithm on dilution series and mixtures of seed and leaf extracts, and estimated recall, precision and F-score of seed and leaf specific features in two experiments of different complexity. CONCLUSION: The new feature detection algorithm meets the requirements of current metabolomics experiments. centWave can detect close-by and partially overlapping features and has the highest overall recall and precision values compared to the other algorithms, matchedFilter (the original algorithm of XCMS) and the centroidPicker from MZmine. The centWave algorithm was integrated into the Bioconductor R-package XCMS and is available from (http://www.bioconductor.org/).
- 38Benton, H. P.; Want, E. J.; Ebbels, T. M. D. Bioinformatics 2010, 26, 2488– 2489 DOI: 10.1093/bioinformatics/btq441Google ScholarThere is no corresponding record for this reference.
- 39Kuhl, C.; Tautenhahn, R.; Bottcher, C.; Larson, T. R.; Neumann, S. Anal. Chem. 2012, 84, 283– 289 DOI: 10.1021/ac202450gGoogle Scholar39CAMERA: An Integrated Strategy for Compound Spectra Extraction and Annotation of Liquid Chromatography/Mass Spectrometry Data SetsKuhl, Carsten; Tautenhahn, Ralf; Boettcher, Christoph; Larson, Tony R.; Neumann, SteffenAnalytical Chemistry (Washington, DC, United States) (2012), 84 (1), 283-289CODEN: ANCHAM; ISSN:0003-2700. (American Chemical Society)Liq. chromatog. coupled to mass spectrometry is routinely used for metabolomics expts. In contrast to the fairly routine and automated data acquisition steps, subsequent compd. annotation and identification require extensive manual anal. and thus form a major bottleneck in data interpretation. Here the authors present CAMERA, a Bioconductor package integrating algorithms to ext. compd. spectra, annotate isotope and adduct peaks, and propose the accurate compd. mass even in highly complex data. To evaluate the algorithms, the authors compared the annotation of CAMERA against a manually defined annotation for a mixt. of known compds. spiked into a complex matrix at different concns. CAMERA successfully extd. accurate masses for 89.7% and 90.3% of the annotatable compds. in pos. and neg. ion modes, resp. Furthermore, the authors present a novel annotation approach that combines spectral information of data acquired in opposite ion modes to further improve the annotation rate. The authors demonstrate the utility of CAMERA in two different, easily adoptable plant metabolomics expts., where the application of CAMERA drastically reduced the amt. of manual anal.
- 40van Creveld, S. G.; Rosenwasser, S.; Schatz, D.; Koren, I.; Vardi, A. ISME J. 2015, 9, 385– 395 DOI: 10.1038/ismej.2014.136Google ScholarThere is no corresponding record for this reference.
- 41Husen, P.; Tarasov, K.; Katafiasz, M.; Sokol, E.; Vogt, J.; Baumgart, J.; Nitsch, R.; Ekroos, K.; Ejsing, C. S. PLoS One 2013, 8, e79736 DOI: 10.1371/journal.pone.0079736Google ScholarThere is no corresponding record for this reference.
- 42Popendorf, K. J.; Fredricks, H. F.; Van Mooy, B. A. S. Lipids 2013, 48, 185– 195 DOI: 10.1007/s11745-012-3748-0Google ScholarThere is no corresponding record for this reference.
- 43Kessner, D.; Chambers, M.; Burke, R.; Agus, D.; Mallick, P. Bioinformatics 2008, 24, 2534– 2536 DOI: 10.1093/bioinformatics/btn323Google Scholar43ProteoWizard: open source software for rapid proteomics tools developmentKessner, Darren; Chambers, Matt; Burke, Robert; Agus, David; Mallick, ParagBioinformatics (2008), 24 (21), 2534-2536CODEN: BOINFP; ISSN:1367-4803. (Oxford University Press)Summary: The ProteoWizard software project provides a modular and extensible set of open-source, cross-platform tools and libraries. The tools perform proteomics data analyses; the libraries enable rapid tool creation by providing a robust, pluggable development framework that simplifies and unifies data file access, and performs std. proteomics and LCMS dataset computations. The library contains readers and writers of the mzML data format, which has been written using modern C++ techniques and design principles and supports a variety of platforms with native compilers. The software has been specifically released under the Apache v2 license to ensure it can be used in both academic and com. projects. In addn. to the library, we also introduce a rapidly growing set of companion tools whose implementation helps to illustrate the simplicity of developing applications on top of the ProteoWizard library.
- 44Kind, T.; Fiehn, O. BMC Bioinf. 2006, 7, 234 DOI: 10.1186/1471-2105-7-234Google Scholar44Metabolomic database annotations via query of elemental compositions: mass accuracy is insufficient even at less than 1 ppmKind Tobias; Fiehn OliverBMC bioinformatics (2006), 7 (), 234 ISSN:.BACKGROUND: Metabolomic studies are targeted at identifying and quantifying all metabolites in a given biological context. Among the tools used for metabolomic research, mass spectrometry is one of the most powerful tools. However, metabolomics by mass spectrometry always reveals a high number of unknown compounds which complicate in depth mechanistic or biochemical understanding. In principle, mass spectrometry can be utilized within strategies of de novo structure elucidation of small molecules, starting with the computation of the elemental composition of an unknown metabolite using accurate masses with errors <5 ppm (parts per million). However even with very high mass accuracy (<1 ppm) many chemically possible formulae are obtained in higher mass regions. In automatic routines an additional orthogonal filter therefore needs to be applied in order to reduce the number of potential elemental compositions. This report demonstrates the necessity of isotope abundance information by mathematical confirmation of the concept. RESULTS: High mass accuracy (<1 ppm) alone is not enough to exclude enough candidates with complex elemental compositions (C, H, N, S, O, P, and potentially F, Cl, Br and Si). Use of isotopic abundance patterns as a single further constraint removes >95% of false candidates. This orthogonal filter can condense several thousand candidates down to only a small number of molecular formulas. Example calculations for 10, 5, 3, 1 and 0.1 ppm mass accuracy are given. Corresponding software scripts can be downloaded from http://fiehnlab.ucdavis.edu. A comparison of eight chemical databases revealed that PubChem and the Dictionary of Natural Products can be recommended for automatic queries using molecular formulae. CONCLUSION: More than 1.6 million molecular formulae in the range 0-500 Da were generated in an exhaustive manner under strict observation of mathematical and chemical rules. Assuming that ion species are fully resolved (either by chromatography or by high resolution mass spectrometry), we conclude that a mass spectrometer capable of 3 ppm mass accuracy and 2% error for isotopic abundance patterns outperforms mass spectrometers with less than 1 ppm mass accuracy or even hypothetical mass spectrometers with 0.1 ppm mass accuracy that do not include isotope information in the calculation of molecular formulae.
- 45Ejsing, C. S.; Duchoslav, E.; Sampaio, J.; Simons, K.; Bonner, R.; Thiele, C.; Ekroos, K.; Shevchenko, A. Anal. Chem. 2006, 78, 6202– 6214 DOI: 10.1021/ac060545xGoogle ScholarThere is no corresponding record for this reference.
- 46Layre, E.; Sweet, L.; Hong, S.; Madigan, C. A.; Desjardins, D.; Young, D. C.; Cheng, T. Y.; Annand, J. W.; Kim, K.; Shamputa, I. C.; McConnell, M. J.; Debono, C. A.; Behar, S. M.; Minnaard, A. J.; Murray, M.; Barry, C. E., 3rd; Matsunaga, I.; Moody, D. B. Chem. Biol. 2011, 18, 1537– 1549 DOI: 10.1016/j.chembiol.2011.10.013Google Scholar46A Comparative Lipidomics Platform for Chemotaxonomic Analysis of Mycobacterium tuberculosisLayre, Emilie; Sweet, Lindsay; Hong, Sunhee; Madigan, Cressida A.; Desjardins, Danielle; Young, David C.; Cheng, Tan-Yun; Annand, John W.; Kim, Keunpyo; Shamputa, Isdore C.; McConnell, Matthew J.; Debono, C. Anthony; Behar, Samuel M.; Minnaard, Adriaan J.; Murray, Megan; Barry, Clifton E., III; Matsunaga, Isamu; Moody, D. BranchChemistry & Biology (Cambridge, MA, United States) (2011), 18 (12), 1537-1549CODEN: CBOLE2; ISSN:1074-5521. (Cell Press)The lipidic envelope of Mycobacterium tuberculosis promotes virulence in many ways, so we developed a lipidomics platform for a broad survey of cell walls. Here we report two new databases (MycoMass, MycoMap), 30 lipid fine maps, and mass spectrometry datasets that comprise a static lipidome. Further, by rapidly regenerating lipidomic datasets during biol. processes, comparative lipidomics provides statistically valid, organism-wide comparisons that broadly assess lipid changes during infection or among clin. strains of mycobacteria. Using stringent data filters, we tracked more than 5,000 mol. features in parallel with few or no false-pos. mol. discoveries. The low error rates allowed chemotaxonomic analyses of mycobacteria, which describe the extent of chem. change in each strain and identified particular strain-specific mols. for use as biomarkers.
- 47Clasquin, M. F.; Melamud, E.; Rabinowitz, J. D. Curr. Protoc Bioinformatics 2012, 37, 14.11.1– 14.11.23 DOI: 10.1002/0471250953.bi1411s37Google ScholarThere is no corresponding record for this reference.
- 48Pearson, A. In Treatise on Geochemistry; Holland, H. D.; Turekian, K. K., Eds.; Elsevier: Oxford, 2014; pp 291– 336.Google ScholarThere is no corresponding record for this reference.
- 49Rosenwasser, S.; Graff van Creveld, S.; Schatz, D.; Malitsky, S.; Tzfadia, O.; Aharoni, A.; Levin, Y.; Gabashvili, A.; Feldmesser, E.; Vardi, A. Proc. Natl. Acad. Sci. U. S. A. 2014, 111, 2740– 2745 DOI: 10.1073/pnas.1319773111Google ScholarThere is no corresponding record for this reference.
- 50Dooley, C. T.; Dore, T. M.; Hanson, G. T.; Jackson, W. C.; Remington, S. J.; Tsien, R. Y. J. Biol. Chem. 2004, 279, 22284– 22293 DOI: 10.1074/jbc.M312847200Google ScholarThere is no corresponding record for this reference.
- 51Hanson, G. T.; Aggeler, R.; Oglesbee, D.; Cannon, M.; Capaldi, R. A.; Tsien, R. Y.; Remington, S. J. J. Biol. Chem. 2004, 279, 13044– 13053 DOI: 10.1074/jbc.M312846200Google ScholarThere is no corresponding record for this reference.
- 52Bligh, E. G.; Dyer, W. J. Can. J. Biochem. Physiol. 1959, 37, 911– 917 DOI: 10.1139/o59-099Google Scholar52A rapid method of total lipide extraction and purificationBligh, E. G.; Dyer, W. J.Canadian Journal of Biochemistry and Physiology (1959), 37 (), 911-17CODEN: CJBPAZ; ISSN:0576-5544.The wet tissue is homogenized with a mixt. of CHCl3 and MeOH to form a miscible system with the H2O in the tissue. Diln. with CHCl3 and H2O seps. the homogenate into 2 layers, the CHCl3 layer contg. all the lipides and the methanolic layer contg. all the non-lipides. A purified lipide ext. is obtained merely by isolating the CHCl3 layer. The method has been applied to fish muscle and may easily be adapted to use with other tissues.
- 53Hummel, J.; Segu, S.; Li, Y.; Irgang, S.; Jueppner, J.; Giavalisco, P. Front. Plant Sci. 2011, 2, 54 DOI: 10.3389/fpls.2011.00054Google Scholar53Ultra performance liquid chromatography and high resolution mass spectrometry for the analysis of plant lipidsHummel Jan; Segu Shruthi; Li Yan; Irgang Susann; Jueppner Jessica; Giavalisco PatrickFrontiers in plant science (2011), 2 (), 54 ISSN:.Holistic analysis of lipids is becoming increasingly popular in the life sciences. Recently, several interesting, mass spectrometry-based studies have been conducted, especially in plant biology. However, while great advancements have been made we are still far from detecting all the lipids species in an organism. In this study we developed an ultra performance liquid chromatography-based method using a high resolution, accurate mass, mass spectrometer for the comprehensive profiling of more than 260 polar and non-polar Arabidopsis thaliana leaf lipids. The method is fully compatible to the commonly used lipid extraction protocols and provides a viable alternative to the commonly used direct infusion-based shotgun lipidomics approaches. The whole process is described in detail and compared to alternative lipidomic approaches. Next to the developed method we also introduce an in-house developed database search software (GoBioSpace), which allows one to perform targeted or un-targeted lipidomic and metabolomic analysis on mass spectrometric data of every kind.
- 54Libiseller, G.; Dvorzak, M.; Kleb, U.; Gander, E.; Eisenberg, T.; Madeo, F.; Neumann, S.; Trausinger, G.; Sinner, F.; Pieber, T.; Magnes, C. BMC Bioinf. 2015, 16, 118 DOI: 10.1186/s12859-015-0562-8Google Scholar54IPO: a tool for automated optimization of XCMS parametersLibiseller Gunnar; Gander Edgar; Trausinger Gert; Sinner Frank; Pieber Thomas; Magnes Christoph; Dvorzak Michaela; Kleb Ulrike; Eisenberg Tobias; Madeo Frank; Madeo Frank; Neumann Steffen; Sinner Frank; Pieber ThomasBMC bioinformatics (2015), 16 (), 118 ISSN:.BACKGROUND: Untargeted metabolomics generates a huge amount of data. Software packages for automated data processing are crucial to successfully process these data. A variety of such software packages exist, but the outcome of data processing strongly depends on algorithm parameter settings. If they are not carefully chosen, suboptimal parameter settings can easily lead to biased results. Therefore, parameter settings also require optimization. Several parameter optimization approaches have already been proposed, but a software package for parameter optimization which is free of intricate experimental labeling steps, fast and widely applicable is still missing. RESULTS: We implemented the software package IPO ('Isotopologue Parameter Optimization') which is fast and free of labeling steps, and applicable to data from different kinds of samples and data from different methods of liquid chromatography - high resolution mass spectrometry and data from different instruments. IPO optimizes XCMS peak picking parameters by using natural, stable (13)C isotopic peaks to calculate a peak picking score. Retention time correction is optimized by minimizing relative retention time differences within peak groups. Grouping parameters are optimized by maximizing the number of peak groups that show one peak from each injection of a pooled sample. The different parameter settings are achieved by design of experiments, and the resulting scores are evaluated using response surface models. IPO was tested on three different data sets, each consisting of a training set and test set. IPO resulted in an increase of reliable groups (146% - 361%), a decrease of non-reliable groups (3% - 8%) and a decrease of the retention time deviation to one third. CONCLUSIONS: IPO was successfully applied to data derived from liquid chromatography coupled to high resolution mass spectrometry from three studies with different sample types and different chromatographic methods and devices. We were also able to show the potential of IPO to increase the reliability of metabolomics data. The source code is implemented in R, tested on Linux and Windows and it is freely available for download at https://github.com/glibiseller/IPO . The training sets and test sets can be downloaded from https://health.joanneum.at/IPO .
- 55Van Mooy, B. A. S.; Fredricks, H. F. Geochim. Cosmochim. Acta 2010, 74, 6499– 6516 DOI: 10.1016/j.gca.2010.08.026Google ScholarThere is no corresponding record for this reference.
- 56Cajka, T.; Fiehn, O. Anal. Chem. 2016, 88, 524– 545 DOI: 10.1021/acs.analchem.5b04491Google Scholar56Toward Merging Untargeted and Targeted Methods in Mass Spectrometry-Based Metabolomics and LipidomicsCajka, Tomas; Fiehn, OliverAnalytical Chemistry (Washington, DC, United States) (2016), 88 (1), 524-545CODEN: ANCHAM; ISSN:0003-2700. (American Chemical Society)A review. Advances in mass spectrometry (MS) and data processing led to discoveries in regulation of cellular metab. by using metabolomics and lipidomics approaches. Mass spectrometry is by far the dominating anal. platform in metabolomics and lipidomics, surpassing the use of NMR at a 5:2 ratio according to the authors' citation anal.
- 57Melamud, E.; Vastag, L.; Rabinowitz, J. D. Anal. Chem. 2010, 82, 9818– 9826 DOI: 10.1021/ac1021166Google ScholarThere is no corresponding record for this reference.
- 58Abida, H.; Dolch, L. J.; Mei, C.; Villanova, V.; Conte, M.; Block, M. A.; Finazzi, G.; Bastien, O.; Tirichine, L.; Bowler, C.; Rebeille, F.; Petroutsos, D.; Jouhet, J.; Marechal, E. Plant Physiol. 2015, 167, 118– 136 DOI: 10.1104/pp.114.252395Google ScholarThere is no corresponding record for this reference.
- 59Levitan, O.; Dinamarca, J.; Zelzion, E.; Lun, D. S.; Guerra, L. T.; Kim, M. K.; Kim, J.; Van Mooy, B. A. S.; Bhattacharya, D.; Falkowski, P. G. Proc. Natl. Acad. Sci. U. S. A. 2015, 112, 412– 417 DOI: 10.1073/pnas.1419818112Google ScholarThere is no corresponding record for this reference.
- 60Clarke, K. R.; Somerfield, P. J.; Gorley, R. N. J. Exp. Mar. Biol. Ecol. 2008, 366, 56– 69 DOI: 10.1016/j.jembe.2008.07.009Google ScholarThere is no corresponding record for this reference.
- 61d’Ippolito, G.; Tucci, S.; Cutignano, A.; Romano, G.; Cimino, G.; Miralto, A.; Fontana, A. Biochim. Biophys. Acta, Mol. Cell Biol. Lipids 2004, 1686, 100– 107 DOI: 10.1016/j.bbalip.2004.09.002Google Scholar61The role of complex lipids in the synthesis of bioactive aldehydes of the marine diatom Skeletonema costatumd'Ippolito, Giuliana; Tucci, Sara; Cutignano, Adele; Romano, Giovanna; Cimino, Guido; Miralto, Antonio; Fontana, AngeloBiochimica et Biophysica Acta, Molecular and Cell Biology of Lipids (2004), 1686 (1-2), 100-107CODEN: BBMLFG; ISSN:1388-1981. (Elsevier B.V.)Diatoms are unicellular plants broadly present in freshwater and marine ecosystems, where they play a primary role in sustaining the marine food chain. In the last 10 years, there has been accumulating evidence that diatoms may have deleterious effects on the hatching success of zooplankton crustaceans, such as copepods, thus affecting dynamics of planktonic populations and limiting secondary prodn. At the mol. level, failure to hatch is ascribed to the presence of a family of inhibitory oxylipins, which we propose to collectively name polyunsatd. short-chain aldehydes (abbreviated here as PUSCAs). Here, the authors describe the origin of PUSCAs produced by the marine diatom Skeletonema costatum via a lipoxygenase-mediated pathways involving non-esterified polyunsatd. fatty acids (PUFA). Expts. with complex lipids proved the pivotal role of chloroplast-derived glycolipids, esp. monogalactosyldiacylglycerol (MGDG), in providing hexadecatrienoic acid (C16:3 ω-4), hexadecatetraenoic acid (C16:4 ω-1) and eicosapentaenoic acid (C20:5 ω-3) to the downstream process leading to 2E,4Z-octadienal (C8:2 ω-4), 2E,4Z,7-octatrienal (C8:3 ω-1) and 2E,4Z-heptadienal (C7:2 ω-3), resp. Under physiol. conditions, the hydrolytic process is assocd. to galactolipid hydrolyzing enzyme capable of removing fatty acids from both sn positions of glycerol.
- 62Mene-Saffrane, L.; Dubugnon, L.; Chetelat, A.; Stolz, S.; Gouhier-Darimont, C.; Farmer, E. E. J. Biol. Chem. 2009, 284, 1702– 1708 DOI: 10.1074/jbc.M807114200Google ScholarThere is no corresponding record for this reference.
- 63Goncalves, E. C.; Wilkie, A. C.; Kirst, M.; Rathinasabapathi, B. Plant Biotechnol. J. 2015, DOI: 10.1111/pbi.12523Google ScholarThere is no corresponding record for this reference.
- 64Merchant, S. S.; Kropat, J.; Liu, B.; Shaw, J.; Warakanont, J. Curr. Opin. Biotechnol. 2012, 23, 352– 363 DOI: 10.1016/j.copbio.2011.12.001Google Scholar64TAG, You're it! Chlamydomonas as a reference organism for understanding algal triacylglycerol accumulationMerchant, Sabeeha S.; Kropat, Janette; Liu, Bensheng; Shaw, Johnathan; Warakanont, JaruswanCurrent Opinion in Biotechnology (2012), 23 (3), 352-363CODEN: CUOBE3; ISSN:0958-1669. (Elsevier B.V.)A review. Photosynthetic organisms are responsible for converting sunlight into org. matter, and they are therefore seen as a resource for the renewable fuel industry. Ethanol and esterified fatty acids (biodiesel) are the most common fuel products derived from these photosynthetic organisms. The potential of algae as producers of biodiesel precursor (or triacylglycerols (TAGs)) has yet to be realized because of the limited knowledge of the underlying biochem., cell biol. and genetics. Well-characterized pathways from fungi and land plants have been used to identify algal homologs of key enzymes in TAG synthesis, including diacylglcyerol acyltransferases, phospholipid diacylglycerol acyltransferase and phosphatidate phosphatases. Many labs. have adopted Chlamydomonas reinhardtii as a ref. organism for discovery of algal-specific adaptations of TAG metab. Stressed Chlamydomonas cells, grown either photoautotrophically or photoheterotrophically, accumulate TAG in plastid and cytoplasmic lipid bodies, reaching 46-65% of dry wt. in starch accumulation (sta) mutants. State of the art genomic technologies including expression profiling and proteomics have identified new proteins, including key components of lipid droplets, candidate regulators and lipid/TAG degrading activities. By analogy with crop plants, it is expected that advances in algal breeding and genome engineering may facilitate realizing the potential in algae.
- 65Hunter, J. E.; Frada, M. J.; Fredricks, H. F.; Vardi, A.; Van Mooy, B. A. S. Front. Mar. Sci. 2015, 2, 81 DOI: 10.3389/fmars.2015.00081Google ScholarThere is no corresponding record for this reference.
- 66Sumner, L.; Amberg, A.; Barrett, D.; Beale, M.; Beger, R.; Daykin, C. Metabolomics 2007, 3, 211– 221 DOI: 10.1007/s11306-007-0082-2Google Scholar66Proposed minimum reporting standards for chemical analysis. Chemical Analysis Working Group (CAWG) Metabolomics Standards Initiative (MSI)Sumner, Lloyd W.; Amberg, Alexander; Barrett, Dave; Beale, Michael H.; Beger, Richard; Daykin, Clare A.; Fan, Teresa W.-M.; Fiehn, Oliver; Goodacre, Royston; Griffin, Julian L.; Hankemeier, Thomas; Hardy, Nigel; Harnly, James; Higashi, Richard; Kopka, Joachim; Lane, Andrew N.; Lindon, John C.; Marriott, Philip; Nicholls, Andrew W.; Reily, Michael D.; Thaden, John J.; Viant, Mark R.Metabolomics (2007), 3 (3), 211-221CODEN: METAHQ; ISSN:1573-3882. (Springer)There is a general consensus that supports the need for standardized reporting of metadata or information describing large-scale metabolomics and other functional genomics data sets. Reporting of std. metadata provides a biol. and empirical context for the data, facilitates exptl. replication, and enables the reinterrogation and comparison of data by others. Accordingly, the Metabolomics Stds. Initiative is building a general consensus concerning the min. reporting stds. for metabolomics expts. of which the Chem. Anal. Working Group (CAWG) is a member of this community effort. This article proposes the min. reporting stds. related to the chem. anal. aspects of metabolomics expts. including: sample prepn., exptl. anal., quality control, metabolite identification, and data pre-processing. These min. stds. currently focus mostly upon mass spectrometry and NMR spectroscopy due to the popularity of these techniques in metabolomics. However, addnl. input concerning other techniques is welcomed and can be provided via the CAWG online discussion forum at http://msi-workgroups.sourceforge.net/ or http://[email protected]. Further, community input related to this document can also be provided via this electronic forum.
- 67Senger, T.; Wichard, T.; Kunze, S.; Gobel, C.; Lerchl, J.; Pohnert, G.; Feussner, I. J. Biol. Chem. 2005, 280, 7588– 7596 DOI: 10.1074/jbc.M411738200Google ScholarThere is no corresponding record for this reference.
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Abstract
Scheme 1
Scheme 1. Preparation, Screening, and Annotation of HPLC-MS Lipid Data Using LOBSTAHSiScheme aWe automate several functions of the ProteoWizard msConvert tool. (43)
Scheme b,cxcms (36-38) was chosen for its command-line features and because it permits follow-on use of the R package CAMERA (39) to identify isotopes.
Scheme dIPO (54) can be used to optimize the values of parameters for some xcms and CAMERA functions.
Scheme eMultiple assignments will likely exist for many peakgroups in a typical data set.
Scheme fThis criterion may be useful when the subject data set contains lipids of exclusively eukaryotic origin.
Scheme gIn the case of C2a, the adduct ion hierarchy for the parent compound is completely satisfied; i.e., the pseudospectrum contains peakgroups representing every adduct ion of the compound of greater theoretical abundance than the least abundant adduct ion present. In the case of C2b, the adduct ion of greatest theoretical abundance and some lesser adduct ion is present, but adduct ions of intermediate abundance are not observed.
Scheme hBoth outcomes may apply simultaneously at this decision point if the data set contains isobars and isomers of the assignment.
Scheme iAnnotation codes (in bold) may be applied as indicated; these are designed to assist the user in evaluating assignment confidence during subsequent data analysis.
Figure 1
Figure 1. (a) All IPL, ox-IPL, and TAG identified in the P. tricornutum data set with high confidence (N = 1039; figure excludes pigments). (b) Distribution by lipid class of high-confidence assignments made in the 0 and 150 μM H2O2 treatments at 24 h (N = 894 and N = 848, respectively). Ellipse size in (b) reflects the number of compounds identified within each class and treatment. The assignments presented in (a) and (b) fully satisfied the LOBSTAHS adduct hierarchy screening criteria (i.e., annotated “C1” or “C2a” according to the logic in Scheme 1) and had no competing assignments, such as possible structural isomers, identified in the data set. Excluded are those compounds having an odd total number of acyl carbon atoms. aGeneral direction of movement within m/z versus RT plot, for a given lipid class and oxidation state. The direction of movement that results from addition or removal of additional oxygen atom(s) varies by lipid class. bNot to scale.
Figure 2
Figure 2. Remodeling of the Phaeodactylum tricornutum lipidome after 24 h, as visualized from data analyzed with LOBSTAHS. (a) Heatmap showing relative abundances across two treatments (0 and 150 μM H2O2) of all IPL, ox-IPL, and TAG identified with high confidence. Each row (N = 896) represents a different compound identified from the database; Figure S11 contains an expanded version of the plot that includes labels for each individual compound. (b) Heatmap detail, showing changes in the most abundant (N = 40) moieties of monogalactosyldiacylglycerol (MGDG), a lipid typically localized to the chloroplast. (c) Fraction of total peak area identified as triacylglycerol (TAG) at three time points during the experiment. Error bars are ± SD of two replicates. In (a) and (b), shading shows the relative abundance of each compound as a fold difference of the mean peak area observed in that treatment from the mean peak area of the compound observed across all treatments. Dendrogram clustering and group definitions were determined by similarity profile analysis. (60) The numbers and identities of the components assigned to each group in (a) are given in Table S5 and Figure S11. Solid black lines in the dendrogram indicate branching that was statistically significant (P ≤ 0.01).
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- 4Strassburg, K.; Huijbrechts, A. M.; Kortekaas, K. A.; Lindeman, J. H.; Pedersen, T. L.; Dane, A.; Berger, R.; Brenkman, A.; Hankemeier, T.; van Duynhoven, J.; Kalkhoven, E.; Newman, J. W.; Vreeken, R. J. Anal. Bioanal. Chem. 2012, 404, 1413– 1426 DOI: 10.1007/s00216-012-6226-x4Quantitative profiling of oxylipins through comprehensive LC-MS/MS analysis: application in cardiac surgeryStrassburg, Katrin; Huijbrechts, Annemarie M. L.; Kortekaas, Kirsten A.; Lindeman, Jan H.; Pedersen, Theresa L.; Dane, Adrie; Berger, Ruud; Brenkman, Arjan; Hankemeier, Thomas; van Duynhoven, John; Kalkhoven, Eric; Newman, John W.; Vreeken, Rob J.Analytical and Bioanalytical Chemistry (2012), 404 (5), 1413-1426CODEN: ABCNBP; ISSN:1618-2642. (Springer)Oxylipins, including eicosanoids, affect a broad range of biol. processes, such as the initiation and resoln. of inflammation. These compds., also referred to as lipid mediators, are (non-) enzymically generated by oxidn. of polyunsatd. fatty acids such as arachidonic acid (AA). A plethora of lipid mediators exist which makes the development of generic anal. methods challenging. Here the authors developed a robust and sensitive targeted anal. platform for oxylipins and applied it in a biol. setting, using HPLC coupled to tandem mass spectrometry (HPLC-MS/MS) operated in dynamic multiple reaction monitoring (dMRM). Besides the well-described AA metabolites, oxylipins derived from linoleic acid, dihomo-γ-linolenic acid, α-linolenic acid, eicosapentaenoic acid and docosahexaenoic acid were included. The authors' comprehensive platform allows the quant. evaluation of ∼100 oxylipins down to low nanomolar levels. Applicability of the anal. platform was demonstrated by analyzing plasma samples of patients undergoing cardiac surgery. Altered levels of some of the oxylipins, esp. in certain monohydroxy fatty acids such as 12-HETE and 12-HEPE, were obsd. in samples collected before and 24 h after cardiac surgery. This generic oxylipin profiling platform can be applied broadly to study these highly bioactive compds. in relation to human disease.
- 5Kuhn, H.; Borngraber, S. In Lipoxygenases and Their Metabolites; Nigam, S.; Pace-Asciak, C. R., Eds.; Kluwer Academic: New York, 1999; pp 5– 28.There is no corresponding record for this reference.
- 6Wenk, M. R. Cell 2010, 143, 888– 895 DOI: 10.1016/j.cell.2010.11.033There is no corresponding record for this reference.
- 7Buseman, C. M.; Tamura, P.; Sparks, A. A.; Baughman, E. J.; Maatta, S.; Zhao, J.; Roth, M. R.; Esch, S. W.; Shah, J.; Williams, T. D.; Welti, R. Plant Physiol. 2006, 142, 28– 39 DOI: 10.1104/pp.106.082115There is no corresponding record for this reference.
- 8Vu, H. S.; Tamura, P.; Galeva, N. A.; Chaturvedi, R.; Roth, M. R.; Williams, T. D.; Wang, X.; Shah, J.; Welti, R. Plant Physiol. 2012, 158, 324– 339 DOI: 10.1104/pp.111.190280There is no corresponding record for this reference.
- 9Andreou, A.; Brodhun, F.; Feussner, I. Prog. Lipid Res. 2009, 48, 148– 170 DOI: 10.1016/j.plipres.2009.02.0029Biosynthesis of oxylipins in non-mammalsAndreou, Alexandra; Brodhun, Florian; Feussner, IvoProgress in Lipid Research (2009), 48 (3-4), 148-170CODEN: PLIRDW; ISSN:0163-7827. (Elsevier Ltd.)A review. Lipid peroxidn. is common to all biol. systems, appearing in developmentally-regulated processes and as a response to environmental changes. Products derived from lipid peroxidn. are collectively named oxylipins. Initial lipid peroxidn. may either occur by enzymic or chem. reactions. An array of alternative reactions further converting lipid hydroperoxides gives rise to a large variety of oxylipin classes, some with reported signaling functions in plants, fungi, algae or animals. The structural diversity of oxylipins is further increased by their occurrence either as esters in complex lipids or as free (non-esterified) fatty acid derivs. The enzymes involved in oxylipin metab. are diverse and comprise a multitude of examples with interesting and unusual catalytic properties. This review aims at giving an overview on plant, fungal, algal and bacterial oxylipins and the enzymes responsible for their biosynthesis.
- 10Thomas, A.; Patterson, N. H.; Marcinkiewicz, M. M.; Lazaris, A.; Metrakos, P.; Chaurand, P. Anal. Chem. 2013, 85, 2860– 2866 DOI: 10.1021/ac3034294There is no corresponding record for this reference.
- 11Haller, E.; Stübiger, G.; Lafitte, D.; Lindner, W.; Lämmerhofer, M. Anal. Chem. 2014, 86, 9954– 9961 DOI: 10.1021/ac502855nThere is no corresponding record for this reference.
- 12Carini, P.; Van Mooy, B. A. S.; Thrash, J. C.; White, A.; Zhao, Y.; Campbell, E. O.; Fredricks, H. F.; Giovannoni, S. J. Proc. Natl. Acad. Sci. U. S. A. 2015, 112, 7767– 7772 DOI: 10.1073/pnas.1505034112There is no corresponding record for this reference.
- 13Van Mooy, B. A. S.; Fredricks, H. F.; Pedler, B. E.; Dyhrman, S. T.; Karl, D. M.; Koblizek, M.; Lomas, M. W.; Mincer, T. J.; Moore, L. R.; Moutin, T.; Rappe, M. S.; Webb, E. A. Nature 2009, 458, 69– 72 DOI: 10.1038/nature07659There is no corresponding record for this reference.
- 14Fulton, J. M.; Fredricks, H. F.; Bidle, K. D.; Vardi, A.; Kendrick, B. J.; DiTullio, G. R.; Van Mooy, B. A. S. Environ. Microbiol. 2014, 16, 1137– 1149 DOI: 10.1111/1462-2920.12358There is no corresponding record for this reference.
- 15Vardi, A.; Van Mooy, B. A. S.; Fredricks, H. F.; Popendorf, K. J.; Ossolinski, J. E.; Haramaty, L.; Bidle, K. D. Science 2009, 326, 861– 865 DOI: 10.1126/science.1177322There is no corresponding record for this reference.
- 16Ianora, A.; Miralto, A. Ecotoxicology 2010, 19, 493– 511 DOI: 10.1007/s10646-009-0434-yThere is no corresponding record for this reference.
- 17Vardi, A. Commun. Integr. Biol. 2008, 1, 134– 136 DOI: 10.4161/cib.1.2.686717Cell signaling in marine diatomsVardi AssafCommunicative & integrative biology (2008), 1 (2), 134-6 ISSN:.Marine photosynthetic microorganisms (phytoplankton) are the basis of marine foodwebs and are responsible for nearly 50% of the global annual carbon-based primary production.1 Phytoplankton can grow rapidly and form massive blooms that can be regulated by environmental factors such as nutrients and light availability and biotic interaction with grazers and viruses.2,3 Their crucial role in drawing down atmospheric CO(2) and their potential use for future biofuel production4 raises the critical need for better understanding of fundamental features of their biology.5 Although traditionally phytoplankton were considered passive drifters with the currents (from Greek-"Planktos"), our recent reports demonstrate how cells employ a complex mechanism to sense changes in environmental cues and activate chemical-based defense strategies.
- 18Pohnert, G. In Algal Chemical Ecology; Amsler, C. D., Ed.; Springer-Verlag: Berlin, 2008; pp 195– 202.There is no corresponding record for this reference.
- 19Casotti, R.; Mazza, S.; Brunet, C.; Vantrepotte, V.; Ianora, A.; Miralto, A. J. Phycol. 2005, 41, 7– 20 DOI: 10.1111/j.1529-8817.2005.04052.xThere is no corresponding record for this reference.
- 20Miralto, A.; Barone, G.; Romano, G.; Poulet, S. A.; Ianora, A.; Russo, G. L.; Buttino, I.; Mazzarella, G.; Laabir, M.; Cabrini, M.; Giacobbe, M. G. Nature 1999, 402, 173– 176 DOI: 10.1038/46023There is no corresponding record for this reference.
- 21Balestra, C.; Alonso-Saez, L.; Gasol, J. M.; Casotti, R. Aquat. Microb. Ecol. 2011, 63, 123– 131 DOI: 10.3354/ame01486There is no corresponding record for this reference.
- 22Ribalet, F.; Intertaglia, L.; Lebaron, P.; Casotti, R. Aquat. Toxicol. 2008, 86, 249– 255 DOI: 10.1016/j.aquatox.2007.11.00522Differential effect of three polyunsaturated aldehydes on marine bacterial isolatesRibalet, Francois; Intertaglia, Laurent; Lebaron, Philippe; Casotti, RaffaellaAquatic Toxicology (2008), 86 (2), 249-255CODEN: AQTODG; ISSN:0166-445X. (Elsevier B.V.)Bioactive polyunsatd. aldehydes (PUAs) are produced by several marine phytoplankton (mainly diatoms) and have been shown to have a detrimental effect on a wide variety of organisms, including phytoplankton and invertebrates. However, their potential impact on marine bacteria has been largely neglected. We assess here the effect of three PUAs produced by marine diatoms: 2E,4E-decadienal, 2E,4E-octadienal and 2E,4E-heptadienal, on the growth of 33 marine bacterial strains, including 16 strains isolated during a bloom of the PUA-producing diatom Skeletonema marinoi in the Northern Adriatic Sea. A concn.-dependent growth redn. was obsd. for 19 bacterial strains at concns. ranging from 3 to 145 μmol L-1. Surprisingly, Eudora adriatica strain MOLA358 (Flavobacteriaceae) and Alteromonas hispanica strain MOLA151 (Alteromonadaceae) showed growth stimulation upon exposure to PUAs at concns. between 13 and 18 μmol L-1. The remaining 12 strains were unaffected by even very high PUA concns. Strains isolated during the diatom bloom showed remarkable resistance to PUA exposures, with only two out of 16 strains showing growth inhibition at PUA concns. below 106, 130, and 145 μmol L-1 for 2E,4E-decadienal, 2E,4E-octadienal and 2E,4E-heptadienal, resp. No correlation between taxonomical position and sensitivity to PUA was obsd. Considering that many bacteria thrive in close vicinity of diatom cells, it is likely that these compds. may shape the structure of assocd. bacterial communities by representing a selection force. This is even more relevant during the final stages of blooms, when senescence and nutrient limitation increase the potential prodn. and release of aldehydes.
- 23Edwards, B. R.; Bidle, K. D.; Van Mooy, B. A. S. Proc. Natl. Acad. Sci. U. S. A. 2015, 112, 5909– 5914 DOI: 10.1073/pnas.1422664112There is no corresponding record for this reference.
- 24Brügger, B. Annu. Rev. Biochem. 2014, 83, 79– 98 DOI: 10.1146/annurev-biochem-060713-03532424Lipidomics: analysis of the lipid composition of cells and subcellular organelles by electrospray ionization mass spectrometryBruegger, BrittaAnnual Review of Biochemistry (2014), 83 (), 79-98CODEN: ARBOAW; ISSN:0066-4154. (Annual Reviews)A review. Lipidomics aims to quant. define lipid classes, including their mol. species, in biol. systems. Lipidomics has experienced rapid progress, mainly because of continuous tech. advances in instrumentation that are now enabling quant. lipid analyses with an unprecedented level of sensitivity and precision. The still-growing category of lipids includes a broad diversity of chem. structures with a wide range of physicochem. properties. Reflecting this diversity, different methods and strategies are being applied to the quantification of lipids. Here, I review state-of-the-art electrospray ionization tandem mass spectrometric approaches and direct infusion to quant. assess lipid compns. of cells and subcellular fractions. Finally, I discuss a few examples of the power of mass spectrometry-based lipidomics in addressing cell biol. questions.
- 25Holčapek, M. Anal. Bioanal. Chem. 2015, 407, 4971– 4972 DOI: 10.1007/s00216-015-8740-025LipidomicsHolcapek, MichalAnalytical and Bioanalytical Chemistry (2015), 407 (17), 4971-4972CODEN: ABCNBP; ISSN:1618-2642. (Springer)There is no expanded citation for this reference.
- 26Andreou, A.; Feussner, I. Phytochemistry 2009, 70, 1504– 1510 DOI: 10.1016/j.phytochem.2009.05.008There is no corresponding record for this reference.
- 27Lamari, N.; Ruggiero, M. V.; d’Ippolito, G.; Kooistra, W. H. C. F.; Fontana, A.; Montresor, M. PLoS One 2013, 8, e73281 DOI: 10.1371/journal.pone.0073281There is no corresponding record for this reference.
- 28Girotti, A. W. J. Lipid Res. 1998, 39, 1529– 1542There is no corresponding record for this reference.
- 29Triantaphylides, C.; Krischke, M.; Hoeberichts, F. A.; Ksas, B.; Gresser, G.; Havaux, M.; Van Breusegem, F.; Mueller, M. J. Plant Physiol. 2008, 148, 960– 968 DOI: 10.1104/pp.108.125690There is no corresponding record for this reference.
- 30Sparvero, L. J.; Amoscato, A. A.; Kochanek, P. M.; Pitt, B. R.; Kagan, V. E.; Bayır, H. J. Neurochem. 2010, 115, 1322– 1336 DOI: 10.1111/j.1471-4159.2010.07055.x30Mass-spectrometry based oxidative lipidomics and lipid imaging: applications in traumatic brain injurySparvero, Louis J.; Amoscato, Andrew A.; Kochanek, Patrick M.; Pitt, Bruce R.; Kagan, Valerian E.; Bayir, HulyaJournal of Neurochemistry (2010), 115 (6), 1322-1336CODEN: JONRA9; ISSN:0022-3042. (Wiley-Blackwell)A review. Lipids, particularly phospholipids, are fundamental to CNS tissue architecture and function. Endogenous polyunsatd. fatty acid chains of phospholipids possess cis-double bonds each sepd. by one methylene group. These phospholipids are very susceptible to free-radical attack and oxidative modifications. A combination of anal. methods including different versions of chromatog. and mass spectrometry allows detailed information to be obtained on the content and distribution of lipids and their oxidn. products thus constituting the newly emerging field of oxidative lipidomics. It is becoming evident that specific oxidative modifications of lipids are crit. to a no. of cellular functions, disease states and responses to oxidative stresses. Oxidative lipidomics is beginning to provide new mechanistic insights into traumatic brain injury which may have significant translational potential for development of therapies in acute CNS insults. In particular, selective oxidn. of a mitochondria-specific phospholipid, cardiolipin, was assocd. with the initiation and progression of apoptosis in injured neurons thus indicating new drug discovery targets. Furthermore, imaging mass-spectrometry represents an exciting new opportunity for correlating maps of lipid profiles and their oxidn. products with structure and neuropathol. This review is focused on these most recent advancements in the field of lipidomics and oxidative lipidomics based on the applicaitons of mass spectrometry and imaging mass spectrometry as they relate to studies of phospholipids in traumatic brain injury.
- 31Spickett, C. M.; Pitt, A. R. Antioxid. Redox Signaling 2015, 22, 1646– 1666 DOI: 10.1089/ars.2014.609831Oxidative Lipidomics Coming of Age: Advances in Analysis of Oxidized Phospholipids in Physiology and PathologySpickett, Corinne M.; Pitt, Andrew R.Antioxidants & Redox Signaling (2015), 22 (18), 1646-1666CODEN: ARSIF2; ISSN:1523-0864. (Mary Ann Liebert, Inc.)Significance: Oxidized phospholipids are now well recognized as markers of biol. oxidative stress and bioactive mols. with both pro-inflammatory and anti-inflammatory effects. While anal. methods continue to be developed for studies of generic lipid oxidn., mass spectrometry (MS) has underpinned the advances in knowledge of specific oxidized phospholipids by allowing their identification and characterization, and it is responsible for the expansion of oxidative lipidomics. Recent Advances: Studies of oxidized phospholipids in biol. samples, from both animal models and clin. samples, have been facilitated by the recent improvements in MS, esp. targeted routines that depend on the fragmentation pattern of the parent mol. ion and improved resoln. and mass accuracy. MS can be used to identify selectively individual compds. or groups of compds. with common features, which greatly improves the sensitivity and specificity of detection. Application of these methods has enabled important advances in understanding the mechanisms of inflammatory diseases such as atherosclerosis, steatohepatitis, leprosy, and cystic fibrosis, and it offers potential for developing biomarkers of mol. aspects of the diseases. Crit. Issues and Future Directions: The future in this field will depend on development of improved MS technologies, such as ion mobility, novel enrichment methods and databases, and software for data anal., owing to the very large amt. of data generated in these expts. Imaging of oxidized phospholipids in tissue MS is an addnl. exciting direction emerging that can be expected to advance understanding of physiol. and disease. Antioxid. Redox Signal. 22, 1646-1666.
- 32Balvers, M. G.; Verhoeckx, K. C.; Bijlsma, S.; Rubingh, C. M.; Meijerink, J.; Wortelboer, H. M.; Witkamp, R. F. Metabolomics 2012, 8, 1130– 1147 DOI: 10.1007/s11306-012-0421-9There is no corresponding record for this reference.
- 33Bruins, M. J.; Dane, A. D.; Strassburg, K.; Vreeken, R. J.; Newman, J. W.; Salem, N., Jr.; Tyburczy, C.; Brenna, J. T. J. Lipid Res. 2013, 54, 1598– 1607 DOI: 10.1194/jlr.M034918There is no corresponding record for this reference.
- 34Ni, Z.; Milic, I.; Fedorova, M. Anal. Bioanal. Chem. 2015, 407, 5161– 5173 DOI: 10.1007/s00216-015-8536-234Identification of carbonylated lipids from different phospholipid classes by shotgun and LC-MS lipidomicsNi, Zhixu; Milic, Ivana; Fedorova, MariaAnalytical and Bioanalytical Chemistry (2015), 407 (17), 5161-5173CODEN: ABCNBP; ISSN:1618-2642. (Springer)Oxidized lipids play a significant role in the pathogenesis of numerous oxidative stress-related human disorders, such as atherosclerosis, obesity, inflammation, and autoimmune diseases. Lipid peroxidn., induced by reactive oxygen and nitrogen species, yields a high variety of modified lipids. Among them, carbonylated lipid peroxidn. products (oxoLPP), formed by oxidn. of the fatty acid moiety yielding aldehydes or ketones (carbonyl groups), are electrophilic compds. that are able to modify nucleophilic substrates like proteins, nucleic acid, and aminophospholipids. Some carbonylated phosphatidylcholines possess pro-inflammatory activities. However, little is known about oxoLPP derived from other phospholipid (PL) classes. Here, the authors present a new anal. strategy based on the mass spectrometry (MS) of PL-oxoLPP derivatized with 7-(diethylamino)coumarin-3-carbohydrazide (CHH). Shotgun MS revealed many oxoLPP derived from in vitro oxidized glycerophosphatidylglycerols (PG, 31), glycerophosphatidylcholine (PC, 23), glycerophosphatidylethanolamine (PE, 34), glycerophosphatidylserines (PS, 7), glycerophosphatidic acids (PA, 17), and phosphatidylinositiolphosphates (PIP, 6) vesicles. This data were used to optimize LipidXplorer-assisted identification, and a python-based post-processing script was developed to increase both throughput and accuracy. When applied to full lipid exts. from rat primary cardiomyocytes treated with peroxynitrite donor SIN-1, ten PL-bound oxoLPP were unambiguously identified by LC-MS, including two PC-, two PE-, one PG-, two PS-, and three PA-derived species. Some of the known carbonylated PC were detected, while most PL-oxoLPP were shown for the first time. [Figure not available: see fulltext.].
- 35R Core Team. R Foundation for Statistical Computing: Vienna, Austria, 2015.There is no corresponding record for this reference.
- 36Smith, C. A.; Want, E. J.; O’Maille, G.; Abagyan, R.; Siuzdak, G. Anal. Chem. 2006, 78, 779– 787 DOI: 10.1021/ac051437y36XCMS: Processing Mass Spectrometry Data for Metabolite Profiling Using Nonlinear Peak Alignment, Matching, and IdentificationSmith, Colin A.; Want, Elizabeth J.; O'Maille, Grace; Abagyan, Ruben; Siuzdak, GaryAnalytical Chemistry (2006), 78 (3), 779-787CODEN: ANCHAM; ISSN:0003-2700. (American Chemical Society)Metabolite profiling in biomarker discovery, enzyme substrate assignment, drug activity/specificity detn., and basic metabolic research requires new data preprocessing approaches to correlate specific metabolites to their biol. origin. Here we introduce an LC/MS-based data anal. approach, XCMS, which incorporates novel nonlinear retention time alignment, matched filtration, peak detection, and peak matching. Without using internal stds., the method dynamically identifies hundreds of endogenous metabolites for use as stds., calcg. a nonlinear retention time correction profile for each sample. Following retention time correction, the relative metabolite ion intensities are directly compared to identify changes in specific endogenous metabolites, such as potential biomarkers. The software is demonstrated using data sets from a previously reported enzyme knockout study and a large-scale study of plasma samples. XCMS is freely available under an open-source license at http://metlin.scripps.edu/download/.
- 37Tautenhahn, R.; Boettcher, C.; Neumann, S. BMC Bioinf. 2008, 9, 504 DOI: 10.1186/1471-2105-9-50437Highly sensitive feature detection for high resolution LC/MSTautenhahn Ralf; Bottcher Christoph; Neumann SteffenBMC bioinformatics (2008), 9 (), 504 ISSN:.BACKGROUND: Liquid chromatography coupled to mass spectrometry (LC/MS) is an important analytical technology for e.g. metabolomics experiments. Determining the boundaries, centres and intensities of the two-dimensional signals in the LC/MS raw data is called feature detection. For the subsequent analysis of complex samples such as plant extracts, which may contain hundreds of compounds, corresponding to thousands of features -- a reliable feature detection is mandatory. RESULTS: We developed a new feature detection algorithm centWave for high-resolution LC/MS data sets, which collects regions of interest (partial mass traces) in the raw-data, and applies continuous wavelet transformation and optionally Gauss-fitting in the chromatographic domain. We evaluated our feature detection algorithm on dilution series and mixtures of seed and leaf extracts, and estimated recall, precision and F-score of seed and leaf specific features in two experiments of different complexity. CONCLUSION: The new feature detection algorithm meets the requirements of current metabolomics experiments. centWave can detect close-by and partially overlapping features and has the highest overall recall and precision values compared to the other algorithms, matchedFilter (the original algorithm of XCMS) and the centroidPicker from MZmine. The centWave algorithm was integrated into the Bioconductor R-package XCMS and is available from (http://www.bioconductor.org/).
- 38Benton, H. P.; Want, E. J.; Ebbels, T. M. D. Bioinformatics 2010, 26, 2488– 2489 DOI: 10.1093/bioinformatics/btq441There is no corresponding record for this reference.
- 39Kuhl, C.; Tautenhahn, R.; Bottcher, C.; Larson, T. R.; Neumann, S. Anal. Chem. 2012, 84, 283– 289 DOI: 10.1021/ac202450g39CAMERA: An Integrated Strategy for Compound Spectra Extraction and Annotation of Liquid Chromatography/Mass Spectrometry Data SetsKuhl, Carsten; Tautenhahn, Ralf; Boettcher, Christoph; Larson, Tony R.; Neumann, SteffenAnalytical Chemistry (Washington, DC, United States) (2012), 84 (1), 283-289CODEN: ANCHAM; ISSN:0003-2700. (American Chemical Society)Liq. chromatog. coupled to mass spectrometry is routinely used for metabolomics expts. In contrast to the fairly routine and automated data acquisition steps, subsequent compd. annotation and identification require extensive manual anal. and thus form a major bottleneck in data interpretation. Here the authors present CAMERA, a Bioconductor package integrating algorithms to ext. compd. spectra, annotate isotope and adduct peaks, and propose the accurate compd. mass even in highly complex data. To evaluate the algorithms, the authors compared the annotation of CAMERA against a manually defined annotation for a mixt. of known compds. spiked into a complex matrix at different concns. CAMERA successfully extd. accurate masses for 89.7% and 90.3% of the annotatable compds. in pos. and neg. ion modes, resp. Furthermore, the authors present a novel annotation approach that combines spectral information of data acquired in opposite ion modes to further improve the annotation rate. The authors demonstrate the utility of CAMERA in two different, easily adoptable plant metabolomics expts., where the application of CAMERA drastically reduced the amt. of manual anal.
- 40van Creveld, S. G.; Rosenwasser, S.; Schatz, D.; Koren, I.; Vardi, A. ISME J. 2015, 9, 385– 395 DOI: 10.1038/ismej.2014.136There is no corresponding record for this reference.
- 41Husen, P.; Tarasov, K.; Katafiasz, M.; Sokol, E.; Vogt, J.; Baumgart, J.; Nitsch, R.; Ekroos, K.; Ejsing, C. S. PLoS One 2013, 8, e79736 DOI: 10.1371/journal.pone.0079736There is no corresponding record for this reference.
- 42Popendorf, K. J.; Fredricks, H. F.; Van Mooy, B. A. S. Lipids 2013, 48, 185– 195 DOI: 10.1007/s11745-012-3748-0There is no corresponding record for this reference.
- 43Kessner, D.; Chambers, M.; Burke, R.; Agus, D.; Mallick, P. Bioinformatics 2008, 24, 2534– 2536 DOI: 10.1093/bioinformatics/btn32343ProteoWizard: open source software for rapid proteomics tools developmentKessner, Darren; Chambers, Matt; Burke, Robert; Agus, David; Mallick, ParagBioinformatics (2008), 24 (21), 2534-2536CODEN: BOINFP; ISSN:1367-4803. (Oxford University Press)Summary: The ProteoWizard software project provides a modular and extensible set of open-source, cross-platform tools and libraries. The tools perform proteomics data analyses; the libraries enable rapid tool creation by providing a robust, pluggable development framework that simplifies and unifies data file access, and performs std. proteomics and LCMS dataset computations. The library contains readers and writers of the mzML data format, which has been written using modern C++ techniques and design principles and supports a variety of platforms with native compilers. The software has been specifically released under the Apache v2 license to ensure it can be used in both academic and com. projects. In addn. to the library, we also introduce a rapidly growing set of companion tools whose implementation helps to illustrate the simplicity of developing applications on top of the ProteoWizard library.
- 44Kind, T.; Fiehn, O. BMC Bioinf. 2006, 7, 234 DOI: 10.1186/1471-2105-7-23444Metabolomic database annotations via query of elemental compositions: mass accuracy is insufficient even at less than 1 ppmKind Tobias; Fiehn OliverBMC bioinformatics (2006), 7 (), 234 ISSN:.BACKGROUND: Metabolomic studies are targeted at identifying and quantifying all metabolites in a given biological context. Among the tools used for metabolomic research, mass spectrometry is one of the most powerful tools. However, metabolomics by mass spectrometry always reveals a high number of unknown compounds which complicate in depth mechanistic or biochemical understanding. In principle, mass spectrometry can be utilized within strategies of de novo structure elucidation of small molecules, starting with the computation of the elemental composition of an unknown metabolite using accurate masses with errors <5 ppm (parts per million). However even with very high mass accuracy (<1 ppm) many chemically possible formulae are obtained in higher mass regions. In automatic routines an additional orthogonal filter therefore needs to be applied in order to reduce the number of potential elemental compositions. This report demonstrates the necessity of isotope abundance information by mathematical confirmation of the concept. RESULTS: High mass accuracy (<1 ppm) alone is not enough to exclude enough candidates with complex elemental compositions (C, H, N, S, O, P, and potentially F, Cl, Br and Si). Use of isotopic abundance patterns as a single further constraint removes >95% of false candidates. This orthogonal filter can condense several thousand candidates down to only a small number of molecular formulas. Example calculations for 10, 5, 3, 1 and 0.1 ppm mass accuracy are given. Corresponding software scripts can be downloaded from http://fiehnlab.ucdavis.edu. A comparison of eight chemical databases revealed that PubChem and the Dictionary of Natural Products can be recommended for automatic queries using molecular formulae. CONCLUSION: More than 1.6 million molecular formulae in the range 0-500 Da were generated in an exhaustive manner under strict observation of mathematical and chemical rules. Assuming that ion species are fully resolved (either by chromatography or by high resolution mass spectrometry), we conclude that a mass spectrometer capable of 3 ppm mass accuracy and 2% error for isotopic abundance patterns outperforms mass spectrometers with less than 1 ppm mass accuracy or even hypothetical mass spectrometers with 0.1 ppm mass accuracy that do not include isotope information in the calculation of molecular formulae.
- 45Ejsing, C. S.; Duchoslav, E.; Sampaio, J.; Simons, K.; Bonner, R.; Thiele, C.; Ekroos, K.; Shevchenko, A. Anal. Chem. 2006, 78, 6202– 6214 DOI: 10.1021/ac060545xThere is no corresponding record for this reference.
- 46Layre, E.; Sweet, L.; Hong, S.; Madigan, C. A.; Desjardins, D.; Young, D. C.; Cheng, T. Y.; Annand, J. W.; Kim, K.; Shamputa, I. C.; McConnell, M. J.; Debono, C. A.; Behar, S. M.; Minnaard, A. J.; Murray, M.; Barry, C. E., 3rd; Matsunaga, I.; Moody, D. B. Chem. Biol. 2011, 18, 1537– 1549 DOI: 10.1016/j.chembiol.2011.10.01346A Comparative Lipidomics Platform for Chemotaxonomic Analysis of Mycobacterium tuberculosisLayre, Emilie; Sweet, Lindsay; Hong, Sunhee; Madigan, Cressida A.; Desjardins, Danielle; Young, David C.; Cheng, Tan-Yun; Annand, John W.; Kim, Keunpyo; Shamputa, Isdore C.; McConnell, Matthew J.; Debono, C. Anthony; Behar, Samuel M.; Minnaard, Adriaan J.; Murray, Megan; Barry, Clifton E., III; Matsunaga, Isamu; Moody, D. BranchChemistry & Biology (Cambridge, MA, United States) (2011), 18 (12), 1537-1549CODEN: CBOLE2; ISSN:1074-5521. (Cell Press)The lipidic envelope of Mycobacterium tuberculosis promotes virulence in many ways, so we developed a lipidomics platform for a broad survey of cell walls. Here we report two new databases (MycoMass, MycoMap), 30 lipid fine maps, and mass spectrometry datasets that comprise a static lipidome. Further, by rapidly regenerating lipidomic datasets during biol. processes, comparative lipidomics provides statistically valid, organism-wide comparisons that broadly assess lipid changes during infection or among clin. strains of mycobacteria. Using stringent data filters, we tracked more than 5,000 mol. features in parallel with few or no false-pos. mol. discoveries. The low error rates allowed chemotaxonomic analyses of mycobacteria, which describe the extent of chem. change in each strain and identified particular strain-specific mols. for use as biomarkers.
- 47Clasquin, M. F.; Melamud, E.; Rabinowitz, J. D. Curr. Protoc Bioinformatics 2012, 37, 14.11.1– 14.11.23 DOI: 10.1002/0471250953.bi1411s37There is no corresponding record for this reference.
- 48Pearson, A. In Treatise on Geochemistry; Holland, H. D.; Turekian, K. K., Eds.; Elsevier: Oxford, 2014; pp 291– 336.There is no corresponding record for this reference.
- 49Rosenwasser, S.; Graff van Creveld, S.; Schatz, D.; Malitsky, S.; Tzfadia, O.; Aharoni, A.; Levin, Y.; Gabashvili, A.; Feldmesser, E.; Vardi, A. Proc. Natl. Acad. Sci. U. S. A. 2014, 111, 2740– 2745 DOI: 10.1073/pnas.1319773111There is no corresponding record for this reference.
- 50Dooley, C. T.; Dore, T. M.; Hanson, G. T.; Jackson, W. C.; Remington, S. J.; Tsien, R. Y. J. Biol. Chem. 2004, 279, 22284– 22293 DOI: 10.1074/jbc.M312847200There is no corresponding record for this reference.
- 51Hanson, G. T.; Aggeler, R.; Oglesbee, D.; Cannon, M.; Capaldi, R. A.; Tsien, R. Y.; Remington, S. J. J. Biol. Chem. 2004, 279, 13044– 13053 DOI: 10.1074/jbc.M312846200There is no corresponding record for this reference.
- 52Bligh, E. G.; Dyer, W. J. Can. J. Biochem. Physiol. 1959, 37, 911– 917 DOI: 10.1139/o59-09952A rapid method of total lipide extraction and purificationBligh, E. G.; Dyer, W. J.Canadian Journal of Biochemistry and Physiology (1959), 37 (), 911-17CODEN: CJBPAZ; ISSN:0576-5544.The wet tissue is homogenized with a mixt. of CHCl3 and MeOH to form a miscible system with the H2O in the tissue. Diln. with CHCl3 and H2O seps. the homogenate into 2 layers, the CHCl3 layer contg. all the lipides and the methanolic layer contg. all the non-lipides. A purified lipide ext. is obtained merely by isolating the CHCl3 layer. The method has been applied to fish muscle and may easily be adapted to use with other tissues.
- 53Hummel, J.; Segu, S.; Li, Y.; Irgang, S.; Jueppner, J.; Giavalisco, P. Front. Plant Sci. 2011, 2, 54 DOI: 10.3389/fpls.2011.0005453Ultra performance liquid chromatography and high resolution mass spectrometry for the analysis of plant lipidsHummel Jan; Segu Shruthi; Li Yan; Irgang Susann; Jueppner Jessica; Giavalisco PatrickFrontiers in plant science (2011), 2 (), 54 ISSN:.Holistic analysis of lipids is becoming increasingly popular in the life sciences. Recently, several interesting, mass spectrometry-based studies have been conducted, especially in plant biology. However, while great advancements have been made we are still far from detecting all the lipids species in an organism. In this study we developed an ultra performance liquid chromatography-based method using a high resolution, accurate mass, mass spectrometer for the comprehensive profiling of more than 260 polar and non-polar Arabidopsis thaliana leaf lipids. The method is fully compatible to the commonly used lipid extraction protocols and provides a viable alternative to the commonly used direct infusion-based shotgun lipidomics approaches. The whole process is described in detail and compared to alternative lipidomic approaches. Next to the developed method we also introduce an in-house developed database search software (GoBioSpace), which allows one to perform targeted or un-targeted lipidomic and metabolomic analysis on mass spectrometric data of every kind.
- 54Libiseller, G.; Dvorzak, M.; Kleb, U.; Gander, E.; Eisenberg, T.; Madeo, F.; Neumann, S.; Trausinger, G.; Sinner, F.; Pieber, T.; Magnes, C. BMC Bioinf. 2015, 16, 118 DOI: 10.1186/s12859-015-0562-854IPO: a tool for automated optimization of XCMS parametersLibiseller Gunnar; Gander Edgar; Trausinger Gert; Sinner Frank; Pieber Thomas; Magnes Christoph; Dvorzak Michaela; Kleb Ulrike; Eisenberg Tobias; Madeo Frank; Madeo Frank; Neumann Steffen; Sinner Frank; Pieber ThomasBMC bioinformatics (2015), 16 (), 118 ISSN:.BACKGROUND: Untargeted metabolomics generates a huge amount of data. Software packages for automated data processing are crucial to successfully process these data. A variety of such software packages exist, but the outcome of data processing strongly depends on algorithm parameter settings. If they are not carefully chosen, suboptimal parameter settings can easily lead to biased results. Therefore, parameter settings also require optimization. Several parameter optimization approaches have already been proposed, but a software package for parameter optimization which is free of intricate experimental labeling steps, fast and widely applicable is still missing. RESULTS: We implemented the software package IPO ('Isotopologue Parameter Optimization') which is fast and free of labeling steps, and applicable to data from different kinds of samples and data from different methods of liquid chromatography - high resolution mass spectrometry and data from different instruments. IPO optimizes XCMS peak picking parameters by using natural, stable (13)C isotopic peaks to calculate a peak picking score. Retention time correction is optimized by minimizing relative retention time differences within peak groups. Grouping parameters are optimized by maximizing the number of peak groups that show one peak from each injection of a pooled sample. The different parameter settings are achieved by design of experiments, and the resulting scores are evaluated using response surface models. IPO was tested on three different data sets, each consisting of a training set and test set. IPO resulted in an increase of reliable groups (146% - 361%), a decrease of non-reliable groups (3% - 8%) and a decrease of the retention time deviation to one third. CONCLUSIONS: IPO was successfully applied to data derived from liquid chromatography coupled to high resolution mass spectrometry from three studies with different sample types and different chromatographic methods and devices. We were also able to show the potential of IPO to increase the reliability of metabolomics data. The source code is implemented in R, tested on Linux and Windows and it is freely available for download at https://github.com/glibiseller/IPO . The training sets and test sets can be downloaded from https://health.joanneum.at/IPO .
- 55Van Mooy, B. A. S.; Fredricks, H. F. Geochim. Cosmochim. Acta 2010, 74, 6499– 6516 DOI: 10.1016/j.gca.2010.08.026There is no corresponding record for this reference.
- 56Cajka, T.; Fiehn, O. Anal. Chem. 2016, 88, 524– 545 DOI: 10.1021/acs.analchem.5b0449156Toward Merging Untargeted and Targeted Methods in Mass Spectrometry-Based Metabolomics and LipidomicsCajka, Tomas; Fiehn, OliverAnalytical Chemistry (Washington, DC, United States) (2016), 88 (1), 524-545CODEN: ANCHAM; ISSN:0003-2700. (American Chemical Society)A review. Advances in mass spectrometry (MS) and data processing led to discoveries in regulation of cellular metab. by using metabolomics and lipidomics approaches. Mass spectrometry is by far the dominating anal. platform in metabolomics and lipidomics, surpassing the use of NMR at a 5:2 ratio according to the authors' citation anal.
- 57Melamud, E.; Vastag, L.; Rabinowitz, J. D. Anal. Chem. 2010, 82, 9818– 9826 DOI: 10.1021/ac1021166There is no corresponding record for this reference.
- 58Abida, H.; Dolch, L. J.; Mei, C.; Villanova, V.; Conte, M.; Block, M. A.; Finazzi, G.; Bastien, O.; Tirichine, L.; Bowler, C.; Rebeille, F.; Petroutsos, D.; Jouhet, J.; Marechal, E. Plant Physiol. 2015, 167, 118– 136 DOI: 10.1104/pp.114.252395There is no corresponding record for this reference.
- 59Levitan, O.; Dinamarca, J.; Zelzion, E.; Lun, D. S.; Guerra, L. T.; Kim, M. K.; Kim, J.; Van Mooy, B. A. S.; Bhattacharya, D.; Falkowski, P. G. Proc. Natl. Acad. Sci. U. S. A. 2015, 112, 412– 417 DOI: 10.1073/pnas.1419818112There is no corresponding record for this reference.
- 60Clarke, K. R.; Somerfield, P. J.; Gorley, R. N. J. Exp. Mar. Biol. Ecol. 2008, 366, 56– 69 DOI: 10.1016/j.jembe.2008.07.009There is no corresponding record for this reference.
- 61d’Ippolito, G.; Tucci, S.; Cutignano, A.; Romano, G.; Cimino, G.; Miralto, A.; Fontana, A. Biochim. Biophys. Acta, Mol. Cell Biol. Lipids 2004, 1686, 100– 107 DOI: 10.1016/j.bbalip.2004.09.00261The role of complex lipids in the synthesis of bioactive aldehydes of the marine diatom Skeletonema costatumd'Ippolito, Giuliana; Tucci, Sara; Cutignano, Adele; Romano, Giovanna; Cimino, Guido; Miralto, Antonio; Fontana, AngeloBiochimica et Biophysica Acta, Molecular and Cell Biology of Lipids (2004), 1686 (1-2), 100-107CODEN: BBMLFG; ISSN:1388-1981. (Elsevier B.V.)Diatoms are unicellular plants broadly present in freshwater and marine ecosystems, where they play a primary role in sustaining the marine food chain. In the last 10 years, there has been accumulating evidence that diatoms may have deleterious effects on the hatching success of zooplankton crustaceans, such as copepods, thus affecting dynamics of planktonic populations and limiting secondary prodn. At the mol. level, failure to hatch is ascribed to the presence of a family of inhibitory oxylipins, which we propose to collectively name polyunsatd. short-chain aldehydes (abbreviated here as PUSCAs). Here, the authors describe the origin of PUSCAs produced by the marine diatom Skeletonema costatum via a lipoxygenase-mediated pathways involving non-esterified polyunsatd. fatty acids (PUFA). Expts. with complex lipids proved the pivotal role of chloroplast-derived glycolipids, esp. monogalactosyldiacylglycerol (MGDG), in providing hexadecatrienoic acid (C16:3 ω-4), hexadecatetraenoic acid (C16:4 ω-1) and eicosapentaenoic acid (C20:5 ω-3) to the downstream process leading to 2E,4Z-octadienal (C8:2 ω-4), 2E,4Z,7-octatrienal (C8:3 ω-1) and 2E,4Z-heptadienal (C7:2 ω-3), resp. Under physiol. conditions, the hydrolytic process is assocd. to galactolipid hydrolyzing enzyme capable of removing fatty acids from both sn positions of glycerol.
- 62Mene-Saffrane, L.; Dubugnon, L.; Chetelat, A.; Stolz, S.; Gouhier-Darimont, C.; Farmer, E. E. J. Biol. Chem. 2009, 284, 1702– 1708 DOI: 10.1074/jbc.M807114200There is no corresponding record for this reference.
- 63Goncalves, E. C.; Wilkie, A. C.; Kirst, M.; Rathinasabapathi, B. Plant Biotechnol. J. 2015, DOI: 10.1111/pbi.12523There is no corresponding record for this reference.
- 64Merchant, S. S.; Kropat, J.; Liu, B.; Shaw, J.; Warakanont, J. Curr. Opin. Biotechnol. 2012, 23, 352– 363 DOI: 10.1016/j.copbio.2011.12.00164TAG, You're it! Chlamydomonas as a reference organism for understanding algal triacylglycerol accumulationMerchant, Sabeeha S.; Kropat, Janette; Liu, Bensheng; Shaw, Johnathan; Warakanont, JaruswanCurrent Opinion in Biotechnology (2012), 23 (3), 352-363CODEN: CUOBE3; ISSN:0958-1669. (Elsevier B.V.)A review. Photosynthetic organisms are responsible for converting sunlight into org. matter, and they are therefore seen as a resource for the renewable fuel industry. Ethanol and esterified fatty acids (biodiesel) are the most common fuel products derived from these photosynthetic organisms. The potential of algae as producers of biodiesel precursor (or triacylglycerols (TAGs)) has yet to be realized because of the limited knowledge of the underlying biochem., cell biol. and genetics. Well-characterized pathways from fungi and land plants have been used to identify algal homologs of key enzymes in TAG synthesis, including diacylglcyerol acyltransferases, phospholipid diacylglycerol acyltransferase and phosphatidate phosphatases. Many labs. have adopted Chlamydomonas reinhardtii as a ref. organism for discovery of algal-specific adaptations of TAG metab. Stressed Chlamydomonas cells, grown either photoautotrophically or photoheterotrophically, accumulate TAG in plastid and cytoplasmic lipid bodies, reaching 46-65% of dry wt. in starch accumulation (sta) mutants. State of the art genomic technologies including expression profiling and proteomics have identified new proteins, including key components of lipid droplets, candidate regulators and lipid/TAG degrading activities. By analogy with crop plants, it is expected that advances in algal breeding and genome engineering may facilitate realizing the potential in algae.
- 65Hunter, J. E.; Frada, M. J.; Fredricks, H. F.; Vardi, A.; Van Mooy, B. A. S. Front. Mar. Sci. 2015, 2, 81 DOI: 10.3389/fmars.2015.00081There is no corresponding record for this reference.
- 66Sumner, L.; Amberg, A.; Barrett, D.; Beale, M.; Beger, R.; Daykin, C. Metabolomics 2007, 3, 211– 221 DOI: 10.1007/s11306-007-0082-266Proposed minimum reporting standards for chemical analysis. Chemical Analysis Working Group (CAWG) Metabolomics Standards Initiative (MSI)Sumner, Lloyd W.; Amberg, Alexander; Barrett, Dave; Beale, Michael H.; Beger, Richard; Daykin, Clare A.; Fan, Teresa W.-M.; Fiehn, Oliver; Goodacre, Royston; Griffin, Julian L.; Hankemeier, Thomas; Hardy, Nigel; Harnly, James; Higashi, Richard; Kopka, Joachim; Lane, Andrew N.; Lindon, John C.; Marriott, Philip; Nicholls, Andrew W.; Reily, Michael D.; Thaden, John J.; Viant, Mark R.Metabolomics (2007), 3 (3), 211-221CODEN: METAHQ; ISSN:1573-3882. (Springer)There is a general consensus that supports the need for standardized reporting of metadata or information describing large-scale metabolomics and other functional genomics data sets. Reporting of std. metadata provides a biol. and empirical context for the data, facilitates exptl. replication, and enables the reinterrogation and comparison of data by others. Accordingly, the Metabolomics Stds. Initiative is building a general consensus concerning the min. reporting stds. for metabolomics expts. of which the Chem. Anal. Working Group (CAWG) is a member of this community effort. This article proposes the min. reporting stds. related to the chem. anal. aspects of metabolomics expts. including: sample prepn., exptl. anal., quality control, metabolite identification, and data pre-processing. These min. stds. currently focus mostly upon mass spectrometry and NMR spectroscopy due to the popularity of these techniques in metabolomics. However, addnl. input concerning other techniques is welcomed and can be provided via the CAWG online discussion forum at http://msi-workgroups.sourceforge.net/ or http://[email protected]. Further, community input related to this document can also be provided via this electronic forum.
- 67Senger, T.; Wichard, T.; Kunze, S.; Gobel, C.; Lerchl, J.; Pohnert, G.; Feussner, I. J. Biol. Chem. 2005, 280, 7588– 7596 DOI: 10.1074/jbc.M411738200There is no corresponding record for this reference.
Supporting Information
Supporting Information
The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.analchem.6b01260.
Methodological details, instructions for obtaining all software and data, supplementary discussion, supplementary data figures, supplementary data tables, and a supplementary chart showing examples of the types of isomers that can be identified with LOBSTAHS (PDF)
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