Metabolic Reprogramming in Leaf Lettuce Grown Under Different Light Quality and Intensity Conditions Using Narrow-Band LEDs

Light-emitting diodes (LEDs) are an artificial light source used in closed-type plant factories and provide a promising solution for a year-round supply of green leafy vegetables, such as lettuce (Lactuca sativa L.). Obtaining high-quality seedlings using controlled irradiation from LEDs is critical, as the seedling health affects the growth and yield of leaf lettuce after transplantation. Because key molecular pathways underlying plant responses to a specific light quality and intensity remain poorly characterised, we used a multi-omics–based approach to evaluate the metabolic and transcriptional reprogramming of leaf lettuce seedlings grown under narrow-band LED lighting. Four types of monochromatic LEDs (one blue, two green and one red) and white fluorescent light (control) were used at low and high intensities (100 and 300 μmol·m−2·s−1, respectively). Multi-platform mass spectrometry-based metabolomics and RNA-Seq were used to determine changes in the metabolome and transcriptome of lettuce plants in response to different light qualities and intensities. Metabolic pathway analysis revealed distinct regulatory mechanisms involved in flavonoid and phenylpropanoid biosynthetic pathways under blue and green wavelengths. Taken together, these data suggest that the energy transmitted by green light is effective in creating a balance between biomass production and the production of secondary metabolites involved in plant defence.


Organ specification
The third leaf

Chemicals
All the chemicals and reagents that were used for this study were of spectrometric grade. Chemicals excluding isotope reference compounds and reagents for silylation were purchased from Sigma Aldrich (Tokyo, Japan), NacalaiTesque (Kyoto, Japan), or Wako Pure Chemical Industries (Osaka, Japan The reagent for trimethylsilylation, N-methyl-N-trimethylsilyltrifluoroacetamide (MSTFA) was purchased from Tokyo Chemical Industry (Tokyo, Japan).

Extraction and derivatization for GC-TOF-MS
Each frozen sample with a 5-mm zirconia bead was extracted with 400 fold amount of solvent (G3 grade) dry nitrogen.

Extraction for LC-q-TOF-MS to detect secondary metabolites
Each frozen sample was extracted with 5 fold amount of solvent (methanol/water [8: containing a reference compound (2.5 µM of 10-camphorsulfonic acid ([M-H] -, m/z 231.0691)) using a mixer mill MM301 (Retsch) at a frequency of 18 Hz for 7 min at 4°C. After centrifugation for 10 min at 17,000 × g, the supernatant was filtered using an Oasis® HLB µElusion plate (30 µm, Waters Co., Massachusetts, US).

Extraction for LC-q-TOF-MS to detect lipids
Each frozen sample was milled using mixer mill MM301 (Retsch) at a frequency of 20 Hz for 2 min at 4°C. After that, frozen powder was extracted with 20 fold volume of extraction solvent

GC-TOF-MS conditions
Using the splitless mode of a CTC CombiPALautosampler (CTC Analytics, Zwingen, Switzerland), Alkane standard mixtures (C8 -C20 and C21 -C40) purchased from Sigma-Aldrich (Tokyo, Japan) were used for calculating the retention index (RI) 1 . For quality control we injected methylstearate into every 6th sample. The sample run order was randomized in single-sequence analyses. We analyzed the standard compound mixtures using the same sequence analysis procedures.

LC-q-TOF-MS conditions to detect secondary metabolites
After preparation of the extracts, the sample extracts (1 µl) were analyzed using an LC interscan delay, 0.1 sec. The data were recorded using Progenesis CoMet (Nonlinear Dynamics).

LC-q-TOF-MS conditions to detect lipids
Sample extracts (1 µl) were analyzed using an LC-MS system equipped with an electrospray (version 2.0) and our custom software for peak-annotation written in JAVA. Peaks were identified or annotated based on their RIs, a comparison of the reference mass-spectra with the GolmMetabolome Database (GMD) released from CSB.DB 3 , and our in-house spectral library. The metabolites were identified by comparison with RIs from the library databases (GMD and our own library) and the RIs of authentic standards. The metabolites were defined as annotated metabolites after comparison with the mass spectra and the RIs from these two libraries. The data matrix was normalized using the CCMN algorithm for further analysis 4 .

Data processing for LC-q-TOF-MS data to detect secondary metabolites
The data matrix was aligned by Progenesis CoMet (Nonlinear Dynamics).For normalization, intensity values of remained peaks was divided by those of the 10-camphorsulfonic acid ([M-H] -, m/z 231.0691) after cutoff of the low-intensity peaks (less than 2000). Metabolite annotation was performed using a literature 5 .

Data processing for LC-q-TOF-MS data to detect lipids
The data matrix was generated using the Makerlynx XS (Waters) using the profiling data files recorded in the MassLynx format (raw). The data matrices were processed using in-house Perl script.

Statistical data analysis for metabolite profile data
The multi-platform data was summarized by unifying metabolite identifiers to a common referencing scheme using the MetMask tool 6 . The three matrices were then concatenated and correlated peaks with the same annotation were replaced by their first principal component. Principal component analysis (PCA) and orthogonal partial least square discriminant analysis (O2PLS-DA) were performed with log10 transformation and autoscaling using SIMCA-P 14.0 software (Umetrics AB, Umeå, Sweden).