2. MATERIALS AND METHODS
2.1. Plant materials and stress treatments
Rice seeds, Oryza sativa Linnaeus, of the Shuhui 498 variety were
placed in barrels that were 27 cm high and 33 cm in diameter, and whole
plants were placed in a Conviron A1000PG artificial climate box in 2018
and 2019 with light quantity of 700
mol.m-2s-1 for a photoperiod of 14 h at a temperature of 29℃
and a temperature of 20℃ during the 10 h night period (humidity: 75%)
when rice was at heading stage. At the beginning of the flowering
period, the rice plants were exposed to a low light treatment, of 233
mol.m-2 s-1 and the control
remaining unchanged, with the other conditions remaining unchanged, and
the glume was removed from the rice grains at the filling stage for RNA
separation at 5, 10 and 15 days after treatment with low light.
2.2 Total RNA extraction, circRNA library construction, and
sequencing
Total RNA was extracted from the control group and low light stress rice
seeds using TRIzol reagent (Invitrogen Corporation, Carlsbad, CA, USA).
The total RNA from each sample was used to prepare the circRNA
sequencing library. After the RNA samples were qualified for detection,
the rRNA was removed from the total RNA sample using the ribo-zero ™
kit. Some long noncoding RNAs (lncRNAs) have the same polyA-tailed
structure as mRNA, so the removal of rRNA can maximize the retention of
lncRNAs containing polyA-tailed RNA. A fragmentation buffer was added to
the enriched RNA to break the RNA into small fragments. Then, a library
was constructed using the segmented RNA as a template, and Qubit 2.0 was
used for initial quantification and dilution of the library. After this,
Agilent 2100 was used to detect the inserted fragment size of the
library. After it was established that the inserted fragment size met
expectations, real-time PCR was used to accurately quantify the
effective concentration of the library to ensure quality of the library.
After successfully passing library detection, different libraries were
pooled into flow cells according to the requirements for effective
concentration and target disembarkation data volume. After cBOT
clustering, the Illumina HiSeqX high-throughput sequencing platform was
used for sequencing.
2.3. Identification of circular RNAs
The filtered transcriptome sequencing data from each sample were
combined, and four software programs, Starchip, CIRI2, CIRCexplorer2,
and CircRNA_finder, were independently used to predict the back-spliced
junction trans-shear site. The predicted circRNA genome sequence
(osa_predict_seq.fa, as the query sequence) was compared with the
known circRNA database genome sequence or transcriptome sequence to
complete the preliminary verification of the circRNA. The Plantcircbase
database, for genome sequences, was used with the Blastn ratio and a
filtering condition of subject coverage greater than 90%, and the
Plantcircnet database, for transcriptome sequences, was also used to
identify circRNA that was located in the intergenic region or single
exon region (i.e., circRNA spans multiple exons) with filtering
conditions of more than 90% similarity and lengths greater than 100.
2.4. Prediction of miRNA targets of circRNAs, mRNA targets of
miRNA, and annotation of functions
The sequence in the middle of the circRNA’s trans-shear site was
extracted, and the reverse sequence was spliced from the middle and
extended by default by 15 nt. The circRNA-miRNA regulatory relationship
was predicted using two software programs, RNAhybrid and TargetFinder,
using sequence-based and free energy-based calculations. The RNAhybrid
result filter parameter that was used was an energy cutoff of -20 and
significant P-value of 0.05. The TargetFinder result filter parameter
that was used was a score of three. Regulatory networks were
constructed, for the control group and low light treatment for the
period 5-10 d after flowering and for the control group for the period
10-15 d after flowering, using differential expression (DE) circRNA-DE
miRNA and DE miRNA-DE mRNA, and enrichment analysis was conducted of the
mRNAs in the network. The filtering conditions of DE circRNA-DE miRNA
were predicted using at least one predictive software. The filtering
condition for the pairs of DE miRNA-DE mRNA effects was that the P-value
of the expression correlation coefficient was less than 0.05 and was
predicted by at least one predictive software program (Supplemental
Figures 1-3). An analysis of targeted messenger RNA functions was
performed using the Gene Ontology (GO), Kyoto Encyclopedia of Genes and
Genomes (KEGG) and Wikipathway databases to predict the processes
involved in rice grain development, such as photosynthesis, sugar and
starch synthesis and metabolism, and the signaling of plant hormones,
such as abscisic acid and auxin. Then the functional annotation results
of the GO, KEGG, and Wikipathway databases were used to analyze the
enrichment of differentially expressed mRNAs in the network.
2.5. Validation of differentially expressed circular RNAs
Quantitative real-time PCR and Sanger sequencing techniques were used to
verify the circular structure and expression patterns of circRNAs
identified using RNA sequences. During validation, 15 differentially
expressed circRNAs were used, of which six were predicted to be sponges
for more than two miRNA, and nine were selected randomly from
differentially expressed circRNAs. Two micrograms of total RNA were used
before real-time quantitative PCR with DNase I (2270 a, Takara,
Japan).Primers were designed to ensure that the circular template was
amplified (Ting, Miao, Gang, & Ting, 2015). The sequence of primers is
shown in Table 1. The expression of circRNAs was quantified on an
ABI
StepOnePlus system (USA) using an SYBR green master mixture (Applied
Biosystems, Foster City, CA, USA). The relative expression rate (△△Ct)
of each circRNA was calculated using the 2 -△△Ctmethod and expressed as log2 of the value, where Ct is
the periodic threshold value of the amplified target or reference gene
(Kennet & Tomas, 2001). The expression of OsActin was used as a
reference for data normalization. SPSS Statistics 19.0 software was used
for statistical analysis. The Student t -test was used to compare
the significant difference between the control and the light treatment
group using a probability level of 0.05.
2.6. Quantification of sucrose and ABA
Approximately 100 mg of developing caryopses was used to quantify the
ABA and sucrose content at the grain-filling stage 5, 10, and 15 days
after treatment in both the light stress and the control groups, using
liquid chromatography-tandem mass spectrometry as described previously
(Mikiko et al., 2009).