Coffea canephora lipid profile
Anagbogu, Chinyere (2020), Coffea canephora lipid profile, Dryad, Dataset, https://doi.org/10.5061/dryad.j6q573nc4
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.
Reddish mature (ripened), coffee beans of these genotypes were collected in ice bags and immediately transferred to -80oC. The endosperms of the coffee bean were excised using sterile blade and re-transferred to -80oC. 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 et al. 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, v/v) (60 µL) (Cajka and Fiehn, 2016; Cajka et al., 2017).
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:H2O 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 m/z; 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 22.214.171.124 was used for data acquisition and analysis. The instrument was tuned and calibrated according to the manufacturer’s recommendations.
Raw data files were converted to the mzML format using the ProteoWizard MSConvert utility. For each m/z values ion chromatogram was extracted with m/z 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.
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 log10 transformed, mean-centered and divided by the standard deviation of each variable before performing statistical analysis.
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