The Bradford protein quantitative kit was used to measure the
concentration of each protein. Lysis buffer, trypsin and TEAB buffer
were added to each protein and digested at 37 °C overnight. Equal volume
of 1% formic acid was mixed with digested protein and then centrifuged.
The supernatant was slowly loaded to the C18 desalting column for
desalting, followed by washing, elution and lyophilized of the protein.
Equal volumes of mixed peptide samples were fractionated using high pH
reverse-phase high performance liquid chromatography (HPLC) coupled with
an Agilent 300 Extend C18 column (5 μm particles, 4.6 mm
ID, 250 mm length) (Wu et al., 2010). Finally, purified peptide samples
with the removal of non-specific adsorbed peptides were collected and
lyophilized for liquid chromatograph-mass spectrometer/mass spectrometer
(LC-MS/MS) detection (Hu et al., 2018). We extracted the coding region
sequences from the transcript assembled above using TransDecoder (Lou et
al., 2018) and then constructed the specified database to search the
MS/MS profiles from each run, and further quantified for peptide
spectrum matches (PSMs) and proteins using Proteome Discoverer™ 2.4
software with SEQUEST® search engine. In order to reduce the false
positive rate, the above-mentioned search results were further filtered
by Proteome Discoverer™ 2.4 software, and those PSMs with confidence
greater than 99% were considered as credible PSMs, and those proteins
containing at least one unique peptide were considered as credible
proteins. A further significance cutoff of FDR < 0.1 was used
for PSMs and proteins. We analyzed the relative quantitative value of
each PSM in 15 samples according to the MS peak area, and then obtained
the relative quantitative value of the unique peptide according to the
PSM relative quantitative value of each peptide (Supplementary
Material). Furthermore, the relative quantitative value of each protein
was identified based on the quantitative information of unique peptides
contained in each protein (Plubell et al., 2017). To explore the
proteins involved in CPL recognition, we used the relative quantitative
value of proteins in the O. oratoria compound eyes exposed to the
DL as a control and determined the differentially expressed proteins
(DEPs) in comparison to the samples exposed to the other four lighting
scenarios (NL, LPL, LCPL, and RCPL). T-test was used to identify
significantly DEPs with a p-value of less than 0.05 (Liu et al., 2020).
To verify the reliability of the proteome data, we selected known opsin
proteins important in light recognition and carried out the subsequent
quantitative analyses. Firstly, peptides were collected using
data-dependent acquisition (DDA) mode to generate mass spectrometry raw
data, and Proteome Discoverer software (version 2.2) was used to search
the generated mass spectrometry data to obtain the unique peptides of
each target protein. Parallel reaction monitoring (PRM) was used to scan
the candidate peptides. Meanwhile, an equal amount of reference peptide
(DSPSAPVNVTVR, the red “V” is the heavy isotope marker) was added to
each sample to correct the peak area. The chromatographic peak of target
peptide was extracted by Skyline software and three daughterions with
high peptide abundance were selected for quantitative analyses. Finally,
the peak areas of target peptides were adjusted according to the peak
area of reference peptide to obtained the relative expression of each
peptide in each sample.
Furthermore, integrate transcriptome and proteome information to
identified the DEGs and DEPs with high correlation in each comparison
paired according to Person correlation coefficient.
Finally, we performed gene function and metabolic pathway enrichment
analyses for significant DEGs and DEPs based on Gene Ontology (GO; Di
Lena et al., 2015) and Kyoto Encyclopedia of Genes and Genomes (KEGG;
Kanehisa et al., 2004) databases to determine which biological functions
are involved in CPL perception. All annotation results were visualized
using R (version 4.2.2).