DOI: 10.5061/dryad.j6q573nc4
title: Coffea canephora lipid profile

description: Coffee (Coffea spp.) is one of the most popular refreshing beverage
 globally. Coffee lipid diversity has untapped potential for improving
 coffee marketability because lipids contribute significantly to both the
 health benefits and cup quality of coffee. However, there have not been
 extensive studies of lipids of C. canephora genotypes. In this study,
 Ultra-performance liquid chromatography coupled with mass spectrometry
 (UPLC–MS) profiling of lipid molecules was performed for 30 genotypes
 consisting of 15 cultivated and 15 conserved genotypes of C. canephora in
 Southwestern Nigeria. We identified nine classes of lipids in the 30
 genotypes which belong to the ‘Niaouli’, ‘Kouillou’ and ‘Java Robusta’
 group: among these, the most abundant lipid class was the
 triacylglycerols, followed by the fatty acyls group. Although ‘Niaouli’
 diverged from the ‘Kouillou’ and ‘Java Robusta’ genotypes when their lipid
 profiles were compared, there was greater similarity in their lipid
 composition by multivariate analysis, compared to that observed when their
 primary metabolites and especially their secondary metabolite profiles
 were examined. However, distinctions could be made among genotypes.
 Members of the fatty acyls group had the greatest power to discriminate
 among genotypes, however, lipids that were low in abundance e.g. a
 cholesterol ester (20:3), and phosphotidylethanolamine (34:0) were also
 helpful to understand the relationships among C. canephora
 genotypes.  The two lipid diversity identified among the C.
 canephora genotypes examined correlated with their overall Single
 Nucleotide Polymorphism diversity assessed by genotype-by-sequencing, will
 be exploited, and included in coffee cup quality improvement.
description: Sample
 Preparation Reddish mature
 (ripened), coffee beans of these genotypes were collected in ice bags and
 immediately transferred to -80<sup>o</sup>C. The endosperms of
 the coffee bean were excised using sterile blade and re-transferred to
 -80<sup>o</sup>C. These endosperms were lyophilized, ground
 into powder with Udy mill (Udy Corporation) and sealed prior to lipidomic
 analysis. The lipid was extracted following the protocols according to
 Matyash <i>et al</i>. Dried extracts containing an internal
 standard [12-[(cyclohexylamino)carbonyl] amino]-dodecanoic acid (CUDA)]
 used as a quality control were resuspended with a mixture of
 methanol/toluene (9:1, <i>v</i>/<i>v</i>) (60 µL)
 (Cajka and Fiehn, 2016; Cajka et al., 2017).   Data
 Acquisition All data
 processing was done at the West Coast Metabolomic Center, University of
 California, Davis. Extracted lipids were separated on an Acquity UPLC CSH
 C18 column (100 × 2.1 mm; 1.7 µm) maintained at 65 °C. The mobile phases
 for positive mode consisted of 60:40 ACN:H<sub>2</sub>O with
 10 mM ammonium formate and 0.1% (v/v) formic acid (A) and 90:10 IPA:ACN
 with 10 mM ammonium formate and 0.1% (v/v) formic acid (B). For negative
 mode, the mobile phase modifier was 10 mM ammonium acetate instead. The
 gradient was as follows: 0 min 85% (A); 0–2 min 70% (A); 2–2.5 min 52%
 (A); 2.5–11 min 18% (A); 11–11.5 min 1% (A); 11.5–12 min 1% (A); 12–12.1
 min 85% (A); and 12.1–15 min 85% (A). Sample temperature was maintained at
 4 °C in the autosampler. Two µL of sample was injected. Vanquish UHPLC
 system (ThermoFisher Scientific) was used. Thermo Q-Exactive HF Orbitrap
 MS instrument was operated in both positive and negative ESI modes
 respectively with the following parameters: mass range
 120−1700 <i>m/z</i>; spray voltage 3.6kV (ESI+) and −3kV
 (ESI−), sheath gas (nitrogen) flow rate 60 units; auxiliary gas (nitrogen)
 flow rate 25 units, capillary temperature 320 °C, full scan MS1 mass
 resolving power 60,000, data-dependent MS/MS (dd-MS/MS) 4 scans per cycle,
 normalized collision energy at 20%, 30%, and 40%, dd-MS/MS mass resolving
 power 15,000. Thermo Xcalibur 4.0.27.19 was used for data acquisition and
 analysis. The instrument was tuned and calibrated according to the
 manufacturer’s recommendations.   Data
 Processing Raw data files
 were converted to the mzML format using the ProteoWizard MSConvert
 utility. For each m/z values ion chromatogram was extracted
 with <i>m</i>/<i>z</i> thresholds of 0.005 Da and
 retention time threshold of 0.10 min. Apex of the extracted ion
 chromatograph was used as peak height value and exported to a .txt file.
 Peak height files for all the samples were merged together to generate a
 data matrix. Targeted peak height signal extraction was performed using an
 R script that is available at https://github.com/barupal.
 Extracted ion chromatograms for each peak were saved as pictures. CSH-POS
 and CSH-NEG data matrices were generated. No normalization was applied as
 minimum signal drift was observed during analysis. Statistical
 analysis Chroma TOF 4.3X software of LECO Corporation
 and LECO-Fiehn Rtx5 database were used for raw peaks exacting, the data
 baselines filtering and calibration of the baseline, peak alignment,
 deconvolution analysis, peak identification and integration of the peak
 area. The RI (retention time index) method was used in the peak
 identification, and the RI tolerance was 5000. Metabolite data were
 normalized by dividing each peak area value by the area of internal
 standard (Ribitol). Data were log<sub>10</sub> transformed,
 mean-centered and divided by the standard deviation of each variable
 before performing statistical analysis. References Matyash, V.; Liebisch, G.;
 Kurzchalia, T.V.; Shevchenko, A.; Schwudke, D. Lipid extraction by
 methyl-tert-butyl ether for high-throughput lipidomics. <i>Journal
 of Lipid Research </i><b>2008</b>,
 <i>49</i>, 1137-1146.   Cajka, T.; Fiehn, O. Increasing
 lipidomic coverage by selecting optimal mobile-phase modifiers in LC-MS of
 blood plasma. <i>Metabolomics </i><b>2016</b>,
 <i>12</i>, doi:ARTN 3410.1007/s11306-015-0929-x.
 Cajka, T.; Smilowitz, J.T.;
 Fiehn, O. Validating Quantitative Untargeted Lipidomics Across Nine Liquid
 Chromatography-High-Resolution Mass Spectrometry Platforms.
 <i>Analytical Chemistry </i><b>2017</b>,
 <i>89</i>, 12360-12368,
 doi:10.1021/acs.analchem.7b03404.   Kind, T.;
 Wohlgemuth, G.; Lee, D.Y.; Lu, Y.; Palazoglu, M.; Shahbaz, S.; Fiehn, O.
 FiehnLib: Mass Spectral and Retention Index Libraries for Metabolomics
 Based on Quadrupole and Time-of-Flight Gas Chromatography/Mass
 Spectrometry. <i>Analytical Chemistry
 </i><b>2009</b>, <i>81</i>,
 10038-10048.