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.