Metabolomics characterization of maize responses to MCB attack
Metabolomics profile was obtained in five biological replicates per each
genotype-treatment combination. A 3 cm portion at the bottom of the stem
internode bellow the ear was taken from each plant. Frozen samples were
lyophilized and ground to a fine powder using an electric mill.
Extraction was performed using 50 mg of this powder through 500 μL of
80% aqueous methanol by sonication for 15 min. Samples were centrifuged
for 10 min in order to remove plant debris (16000 × g, at room
temperature). Supernatant was filtered through a 0.20 µm micropore PTFE
membrane and placed in vials for further analysis. Five μL of each
sample were injected into an ultra‐high‐performance liquid
chromatography (UHPLC) system (Thermo Dionex Ultimate 3000 LC) connected
to a QTOF detector (Bruker Compact™) with a heated electrospray
ionization (ESI) source. Chromatographic separation was performed in a
Intensity Solo 2 C18 column (2.1 × 100 mm 1.7 µm pore size) (Bruker
Daltonics, Germany) using a binary gradient solvent mode consisting of
0.1% formic acid in water (solvent A) and acetonitrile (solvent B). The
following gradient was used: 3% B (0–3 min), from 3% to 25% B
(3–10 min), from 25% to 80% B (10–18 min), from 80% to 100% B
(18–22 min), and held at 100% B until 24 min. The flow rate was
established at 0.4 mL min−1 and column temperature was controlled at
35 °C.
MS data were acquired using an acquisition rate of 2 Hz over the mass
range of 50–1200 m/z. Both polarities (±) of ESI mode were used under
the following specific conditions: gas flow 9 L min−1; nebulizer
pressure 2.6 bar; dry gas 9 L min−1; dry temperature 220 °C. Capillary
and endplate offset were set to 4500 and 500 V, respectively. To monitor
the performance of data acquisition, the run sequence was started with
three blanks (methanol, the solvent used in sample extraction) and a
standard compound (triphenyl phosphate in positive ionization mode and
chloramphenicol in negative ionization mode). Auto MS/MS fragmentation
in pooled samples was performed in order to facilitate compound
identification. For MS/MS analysis data was acquire using an acquisition
rate of 8 Hz and precursor ions collected using an absolute threshold of
1500 counts and a cycle time of 1.0 s.
The algorithm T-Rex 3D from the MetaboScape 4.0 software (Bruker
Daltoniks, Germany) was used for peak alignment and detection. The
generated dataset was imported into Metaboanalyst (Chong, Wishart &
Xia, 2019) to perform statistical analyses. In order to remove
non‐informative variables, data was filtered using the interquantile
range filter (IQR). Moreover, Pareto variance scaling was used to remove
the offsets and adjust the importance of high‐ and low‐abundance ions to
an equal level. The resulting three‐dimensional matrix (peak indices,
samples and variables) was further subjected to multivariate data
analysis. Within each inbred, partial least squares discriminant
analysis (PLS‐DA) was carried out to investigate and visualize the
pattern of metabolite changes between the control and each infestation
treatment. This analysis was applied to obtain an overview of the
complete dataset and discriminate those variables that are responsible
for variation between groups (control vs 48-hours feeding to
characterize the short-term response to MCB attack and control vs9-days feeding to characterize the long-term response). The PLS‐DA model
was evaluated through a cross‐validation (R2 and Q2 parameters). The
quality assessment (Q2) and R2 statistics provide a qualitative measure
of consistency between the predicted and original data or, in other
words, estimates the predictive ability of the model.
For each inbred and infestation treatment, features with a VIP (variable
importance in projection) score >2 in the PLS‐DA model
(control vs infestation treatment) were selected and considered
as the most influential features in that inbred response to MCB attack.
In many studies, a VIP value >2 is a correct threshold for
feature selection, but this cut‐off depends on the number of variables
used. For tentative identification, accurate mass data and isotopic
pattern distributions for the precursor and product ions (if available)
were studied and compared to the spectral data of reference compounds
with a mass tolerance of 5 ppm. PubChem(Kim, Chen, Cheng, Gindulyte, He,
He, Li, Shoemaker, Thiessen, Yu, Zaslavsky, Zhang & Bolton, 2019),
MassBank (Horai, Arita, Kanaya, Nihei, Ikeda, Suwa, Ojima, Tanaka,
Tanaka, Aoshima, Oda, Kakazu, Kusano, Tohge, Matsuda, Sawada, Hirai,
Nakanishi, Ikeda, Akimoto, Maoka, Takahashi, Ara, Sakurai, Suzuki,
Shibata, Neumann, Iida, Funatsu, Matsuura, Soga, Taguchi, Saito &
Nishioka, 2010), KEGG(Kanehisa, Sato, Kawashima, Furumichi & Tanabe,
2016), KNApSAcK (Afendi, Okada, Yamazaki, Hirai-Morita, Nakamura,
Nakamura, Ikeda, Takahashi, Altaf-Ul-Amin, Darusman, Saito & Kanaya,
2012), Metlin (Guijas, Montenegro-Burke, Domingo-Almenara, Palermo,
Warth, Hermann, Koellensperger, Huan, Uritboonthai, Aisporna, Wolan,
Spilker, Benton & Siuzdak, 2018), and Chemspider (Ayers, 2012)
databases were used.