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).