2.6 Identification of circularly polarized light (CPL) recognition-related functional genes and proteins
The purified mRNA was cleaved into appropriate size fragments using the Fragmentation buffer (Agilent) and then used for cDNA libraries construction. We sequenced all cDNA libraries using the Illumina NovaSeq 6000 sequencing platform across one lane with paired-end 150 bp. The raw transcriptome sequencing reads were trimmed using fastp software (version 0.20.1; Chen et al., 2018) and low-quality paired-end sequencing reads with sequencing adaptors, unknown nucleotide (ratio > 10%), and low quality (if Qphred ≤ 20 over 50%) were eliminated. The high-quality sequencing reads from 15 O. oratoria s were thende novo assembled to unigene sequences by Trinity (version 2.0.6; Grabherr et al., 2011) software (Supplementary Material). The BUSCO software (version 3.0.2; Simão et al., 2015) was evaluated for the completeness of the assembled unigene sequences (Supplementary Material). We set the lighting scenario as an independent variable and then used BWA-mem (Li and Durbin, 2009) to map each set of high-quality sequencing reads to the unigenes sequences to calculate the gene expression level of the O. oratoria compound eyes in the 5 lighting scenarios. FPKM (Fragments per kilobase of exon model per million mapped fragments) values (Langmead and Salzberg, 2012) were used to normalize the gene expression levels. Considering that the expression variation of functional genes will affect the response ability of the O. oratoria compound eye to different lighting scenarios, we treated the FPKM of the O. oratoria s compound eyes under DL as a control and then quantified the differentially expressed genes (DEGs) of the compound eyes exposed to other four lighting scenarios (NL, LPL, LCPL, and RCPL) using edgeR package (Lou et al., 2019). The filtering thresholds for significantly DEGs were conservative and as follows: false discovery rate (FDR) ≤ 0.01 and |log2FoldChange| ≥ 2. To verify the reliability of the transcriptome data, we screened 11 opsin genes (Table.1) and then performed quantitative reverse transcription PCR (qRT-PCR) experiments. Two stably expressed reference genes (EF1αand Tubu ) were applied to correct eleven target genes. Primer Premier 6.0 software was used to design the 11 target genes- and 2 reference genes- specific primers (Table.1). The 15 mRNAs used for transcriptome sequencing were reverse-transcribed into cDNA and diluted 25 times to obtain cDNA templates for qRT-PCR experiments. All the qRT-PCR experiments were performed on the CFX96 Touch Real-Time PCR Detection system following the instructions of QuantiNova SYBR Green PCR Kit. Three technical replicates were carried out for each qRT-PCR reaction, and u2-ΔΔCT (ΔCT = CTtarget genes – CTreference gene, ΔΔCT = ΔCT(NL, LPL, RCPL, LCPL) – ΔCTNL) was used to calculate the relative expression of 11 target genes.
Table.1. Primer sequences specific to reference genes and target genes.