Screening radiation-induced differential expressed circular RNAs and establishing the expression models in human lymphoblastoid cell line AHH-1 induced by 60Co γ-rays

Abstract

After a large-scale radiological accident, such as Chernobyl or Fukushima Nuclear Power Plant accident occurred, rapid and high-throughput biodosimetry would be needed. It is very important to find a rapid, high-throughput biodosimeter for massive population triage and biological dose estimation. Studies showed that Circular RNA (circRNA) expressions can be altered by ionizing radiation in normal human cell lines and tumor tissue. Whether circRNAs are suitable for triage and dose estimation remains unclear. In this study, radiation-induced differential expressed circRNAs were screened through transcriptome sequencing with human lymphoblastoid cell line AHH-1 at 4 h after irradiated with 0, 2, and 5 Gy Cobalt-60 γ-rays. The results showed that 3 up-regulated and 4 down-regulated circRNAs were identified in 2 Gy-induced cells, and 5 up-regulated and 3 down-regulated circRNAs were identified in 5 Gy-induced cells both compared with those in the 0 Gy group. After validation, 11 circRNAs were chosen for establishing the expression dosimetry models, because their expression levels changed in a dose-dependent manner. Different circRNA expression models involving one or two circRNAs were established by stepwise regression analysis for different time-point (4h, 12 h, 24 h, and 48 h) post-irradiation, with R² ranged from 0.950 to 0.998 (P < 0.01). A blind test showed that most of the estimated doses based on the expression models were deviated from the actual absorbed doses and the relative deviation were higher than 20%. In conclusion, ionizing radiation can alter the circRNA expression profile in the normal cell line AHH-1. Some circRNAs may be having the potential for being radiation biomarkers and needs further comprehensive investigation.

Introduction

In a large-scale radiological incident, thousands of people might be exposed to ionizing radiation, rapid and high-throughput biodosimetry would be needed for dose estimation for the victims. The dicentric chromosome plus ring and micronucleus are both the traditional radiation biodosimeters. Although there have been ongoing attempts to automatically analyse these two end points via automated analysis system, specimen preparation takes at least two or three days and the quality of specimen also influences the accuracy of analysis. It is urgent to find new biomarkers which can rapidly and high-throughput estimate the absorbed doses of large populations. In recent years, several molecular biology markers, such as message RNAs (mRNAs), microRNAs, or proteins, were reported that ionizing radiation-induced their expression changes followed a dose-dependent manner^1–3. However, the half-lives of these materials are usually short. If body fluid samples from the victims are collected several days after a nuclear accident, these molecular biology markers may not accurately reflect the exposure doses because of their short half-lives. Therefore, it still urgent to find new biomarker for estimating the exposure doses of large populations rapidly.

Circular RNAs (circRNAs), like microRNAs and lncRNAs, is a kind of novel endogenous non-coding RNA with regulatory function^4,5. CircRNAs are mainly generated from precursor mRNA (pre-mRNA) of protein-encoding genes through the back-splicing process⁶. Unlike the canonical splicing mechanism, the splicing complex inversely links the 3’-end splicing donor site at downstream of the pre-mRNA to the upstream 5’-end splicing acceptor site, thereby forming a covalently closed loop-shaped molecule⁷. Therefore, circRNAs do not have the 5’-end cap and the 3’-end poly (A) tail of the mature mRNA, that is, circRNAs have no free ends⁶. This character of molecular structure makes circRNAs tolerant to ribonuclease R (RNase R) digestion^{8, 9}. RNase R is an RNA enzyme that can digest linear RNA molecules but cannot easily digest RNAs with circular structures, lasso structures, or double-stranded RNA molecules with less than 7 nucleotides in the 3’ protruding termini⁹. Some researches had verified that the half-lives of circRNAs were longer than that of their corresponding linear RNAs^{10, 11}. According to the sequence of circRNAs, circRNAs can be categorized into three types as exon circRNAs (ecircRNAs)¹², exon-intron cirRNAs (eiciRNAs)¹³, and intronic circRNAs (ciRNAs)¹⁴.

The expression of circRNAs is cell-specific, tissue-specific, and developmental stage-specific^15–17. In normal physiological and pathological states, circRNAs play critical roles at molecular, cellular, and tissue levels via participating in various biological pathways, such as transcriptional regulation, translation regulation, and protein degradation^18–20. Due to the characteristics itself, circRNAs have the potential for being as biomarkers. Some studies reported that circRNAs could be biomarkers for cancer diagnosis and prognosis. Chen et al. found that hsa_circ_0000190 was down-regulated in gastric cancer tissue and plasma compared with that in the normal tissue adjacent tumor. They also found hsa_circ_0000190 expression level had better sensitivity and specificity than the two classic gastric cancer biomarkers including carcinoembryonic antigen and CA19-9. Hsa_circ_0000190 had a potential to be a diagnostic biomarkers of gastric cancer²¹. Hsa_circ_0001946 was confirmed to be a diagnostic and prognostic biomarker of esophageal squamous cell cancer²². Hsa_circ_0001649 and hsa_circ_0078602 may have the potential as biomarkers for hepatocellular carcinoma diagnosis and/or prognosis^{23, 24}. CircRNAs could not only be biomarkers for cancer but also be biomarkers for non-cancerous diseases. Some circRNAs could use as diagnostic biomarkers for cardiovascular disease, neurological disease, renal disease, and autoimmune disease^25–27. CircRNAs as disease biomarkers has been studied extensively. Recent studies have confirmed ionizing radiation can alter the circRNA expression profiles^28–30. However, it is still unknown whether circRNAs could be radiation biomarkers.

In this study, the radiation-induced differential expressed circRNAs (DE-circRNAs) were screened by transcriptome sequencing and bioinformatic analysis in human lymphoblastoid cell line AHH-1, which exposed to 0, 2, and 5 Gy of ⁶⁰Co γ-rays. After validation, the dose-response relationships between ionizing radiation dose and candidate circRNA expression levels were investigated at different time-points after irradiation. The results indicated that these radiation-induced DE-circRNAs may have the potential to become radiation biomarkers.

Results

Circular RNA profiles in AHH-1 cells exposed to 0, 2, and 5 Gy ⁶⁰Co γ-rays. To investigate the effect of ionizing radiation on the expression level of circRNAs in AHH-1, the expression profiles of circRNAs in AHH-1 cells, which exposed to different doses, were identified via transcriptome sequencing. If the circRNA reads were found in at least two parallel samples, this circRNA was detected. Based on this, 883 circRNAs were quantitated in the 0 Gy group. And 1116 circRNAs and 1248 circRNAs were quantitated in the 2 Gy and 5 Gy groups. Of them, 505 circRNAs were identified both in 0 Gy and 2 Gy groups (Fig. 1a). The average length of these circRNAs was 806 nt, consisting of 93.8% ecircRNAs, 4.2% eiciRNAs, and 2.0% ciRNAs. Five hundred and forty-five circRNAs were identified both in 0 Gy and 5 Gy groups (Fig. 2a). The average length of these circRNAs was 785 nt, consisting of 94.3% ecircRNAs, 4.4% eiciRNAs, and 1.3% ciRNAs.

Seven DE-circRNAs were identified in the 2 Gy group compared with the 0 Gy group, consisting of 3 up-regulated and 4 down-regulated circRNAs (Fig. 1b, 1c) (fold-change > 1.5, P < 0.05). These DE-circRNAs consisted of 6 ecircRNAs and 1 ciRNA (Fig. 1d). Eight DE-circRNAs were identified in the 5 Gy group compared with the 0 Gy (Fig. 2b, 2c). In these DE-circRNAs, the expressions of 5 circRNAs were up-regulated and 3 circRNAs expressions were down-regulated. All the DE-circRNAs in the 5 Gy groups were ecircRNAs (Fig. 2d). To sum up, 14 DE-circRNAs were screened in radiation groups. The expression levels of hsa_circZFAND6_008 in both radiation groups were lower than that of in the control group (P < 0.05).

Validation of radiation DE-circRNAs. To verify whether the expression levels of the 14 circRNAs candidates selected by transcriptome sequencing were affected by ionizing radiation, real-time PCR was used for validation. Because of the specific molecular structure of circRNAs, the amplified primers need to span the back junction site and back-to-back. Since circRNAs come from protein-encoding genes, their sequence shows strong similarity to the corresponding mRNA of the same host genes. To avoid amplifying linear transcripts of circRNAs, the specificity of the primers was therefore tested before performing the validation experiments. The products amplified by circRNA primers or mRNA primers were quantified in all samples digested with or without RNase R (RNase R + and RNase R-). As a result, the relative expressions of products amplified by circRNA primers (except for hsa_circZDHHC21_004) were not statistically down-regulated between RNase R + groups and RNase R- groups (Fig. 3a), whereas the expression of the corresponding mRNAs decreased significantly with RNase R digestion (P < 0.01) (Fig. 3b). It indicated that the template of products amplified by the designed back-to-back primers were indeed circRNAs. The back-to-back primers can be used for subsequent validation experiments. Although the expression of hsa_circZDHHC21_004 decreased significantly after RNase R treatment (P < 0.05), the decrease fold was significantly smaller than that of the cognate linear transcript. So the primers for hsa_circZDHHC21_004 could also be used for subsequent validation experiments.

At 4 h and 24 h after irradiation, the relative expressions of 14 candidates were detected. At 4h post exposed to 2 Gy, hsa_circPCMTD1_004 and hsa_circSPECC1_006 expression levels were significantly up-regulated and the expression level of hsa_circZFAND6_008 was significantly down-regulated, consistent with the transcriptome sequencing results (Fig. 4a) (P < 0.01, P < 0.05 ). The expression levels of hsa_circFBXW7_009 and hsa_circZDHHC21_004 increased significantly, contrary to the transcriptome sequencing results (Fig. 4a) (P < 0.01, P < 0.05). The changes of hsa_circUHRF2_003 and hsa_circTCONS_00017881_003 expression were no statistical significance (Fig. 4a). At 24h after 2 Gy irradiation, the expression levels of hsa_circPCMTD1_004 and hsa_circSPECC1_006 were still increased, which were consistent with the transcriptome sequencing results (Fig. 4b) (P < 0.01). The relative expression of hsa_circZFAND6_008, hsa_circFBXW7_009, and hsa_circZDHHC21_004 were also increased, which was contrary to the transcriptome sequencing results (Fig. 4b) (P < 0.01). Hsa_circUHRF2_003 and hsa_circTCONS_00017881_003 expressions were still unchanged.

At 4 h and 24 h post 5 Gy irradiation, hsa_circFAM13B_024, hsa_circMPP6_020, and hsa_circATP5C1_006 expression levels were up-regulated, coincided with the transcriptome sequencing results (Fig. 4c, 4d) (P < 0.01). The expression levels of hsa_circPLOD2_001, hsa_circZFAND6_008, and hsa_circXPO1_021 were decreased significantly, which were reverse to the transcriptome sequencing results (Fig. 4c, 4d) (P < 0.01). The hsa_circBARD1_018 expression level remained unchanged at 4h and increased significantly at 24h (Fig. 4c, 4d) (P < 0.01). The hsa_circNFATC3_003 expressions were no statistically different from those of the control both at 4 h and 24 h after exposure (Fig. 4c, 4d). The above results indicated that the expression levels of 11 out of 14 candidate circRNAs were affected by ionizing radiation, although some of them showed variable trends in contrast to the transcriptome sequencing results. Therefore, these 11 circRNAs have been used for subsequent studies.

Dose-response relationships between ionizing radiation dose and circRNA expressions levels. To test whether the above radiation DE-circRNAs have dose-response effects, the expression levels of the 11 circRNA candidates were further detected in AHH-1 cells at 4 h, 12 h, 24 h, and 48 h after exposed to 0, 2, 4, 6, and 8 Gy ⁶⁰Co γ -rays. The dose-response relationship between the absorbed doses and the single circRNA expression level was analysed by linear regression analysis. At 4 h after irradiation, the changes of expression levels of 7 circRNAs were overall up-regulated with absorbed doses (Fig. 5b, 5c, 5e-5h, 5k). Of them, the expression of hsa_circFBXW7_009, hsa_circFAM13B_024, and hsa_circPLOD2_001 varied with absorbed doses according to a linear model (Supplementary Table S2). There were no linear relationships between three circRNA expression levels and the absorbed doses, which their expression levels decreased firstly and then increased with the absorbed doses at 4 h post-irradiation (Fig. 5a, 5i, 5j) (Supplementary Table S2). The expression level of hsa_circZFAND6_008 was down-regulated with absorbed doses in a linear model at 4 h after irradiation (Fig. 5d) (Supplementary Table S2). There were also 7 circRNAs whose expression levels were overall up-regulated with absorbed doses at 12 h after irradiation (Fig. 5a-5d, 5f, 5i, 5k). Of them, the expression levels of hsa_circZFAND6_008, hsa_circMPP6_020, and hsa_circPLOD2_001 changed with the absorbed doses following a linear model (Supplementary Table S2). The expression levels of hsa_circZDHHC21_004, hsa_circFAM13B_024, hsa_circATP5C1_006 and hsa_circXPO1_021 were not consistently increased with absorbed doses at 12 h after exposure (Fig. 5e, 5g-5h, 5j). At 24 h after irradiation, 8 circRNAs expression levels gradually increased with absorbed doses followed a linear model (Fig. 5a-5d, 5f-5g, 5i-5j)(Supplementary Table S2). The expression level of Hsa_circZDHHC21_004 was also not stably increased with the absorbed doses at 24 h after radiation exposure (Fig. 5e). The change trend of hsa_circATP5C1_006 expression with the absorbed doses was not significant at 24 h post-irradiation (Fig. 5h). The expression of hsa_circPLOD2_001 was gradually up-regulated from 0 Gy to 6 Gy, but down-regulated at 8 Gy at 24 h after radiation exposure (Fig. 5k). At 48 h after irradiation, the expressions levels of hsa_circFBXW7_009, hsa_circFAM13B_024, and hsa_circPLOD2_001 were significantly increased with absorbed doses in a linear model (Fig. 5c, 5g, 5k) (Supplementary Table S2). The expression of hsa_circBARD1_018 was up-regulated induced by ionizing radiation, but not dose dependent (Fig. 5f). The changed trends of other circRNAs expressions were not stable, some of them were up-regulated firstly but down-regulated later (Fig. 5b, 5d, 5i, 5j) others were fluctuated with the absorbed doses (Fig. 5a, 5e, 5h).

It may be not accurate for dose-estimation by using a single circRNA. Therefore, the expression dosimetry models with multiple circRNAs were established at different time points after ionizing radiation by using of stepwise regression analysis (Table 1). At 4 h after radiation exposure, only the hsa_circPLOD2_001 expression level was included in the model (P < 0.01). Hsa_circPLOD2_001 and hsa_circZFAND6_008 were included in the expression model at 12 h post-irradiation (P < 0.01). The adjusted R² value of 12 h expression model was greater than that of each single circRNA at the same time point. Collinearity diagnostics analysis showed no collinearity between variables (VIF < 10). The expression model of 24 h after irradiation, hsa_circPCMTD1_004 and hsa_circZFAND6_008 were included (P < 0.01). The adjusted R² value of 24 h expression model was also greater than that of each single circRNA. Collinearity diagnostic analysis also showed no collinearity between variables (VIF < 10). Only hsa_circPLOD2_001 was included in the expression model at 48 h after irradiation.

The accuracy of above expression models were validated in blind tests (Supplementary Table S3). Although all the adjusted R² of the each expression model was more than 0.900, most of the estimated doses by using of expression models were deviated from the actual absorbed doses and the relative deviation were higher than 20%. Only the estimated dose in the 3 Gy group at 48 h post-irradiation were similarly to the actual dose, which the relative deviation was less than 10%.

Discussion

In this study, it was found that ionizing radiation could alter the circRNA expression profile in human lymphoblastoid cell line AHH-1. Eleven radiation DE-circRNAs were screened and validated. Some circRNA expression levels increased with absorbed doses in a linear model. At different time after irradiation, 4 multiple circRNA expression models were established. Hsa_circPLOD2_001, hsa_circZFAND6_008, and hsa_circPCMTD1_004 had the potential to become radiation biomarkers.

Studies about circRNAs in radiation biology effects are mainly focused on two aspects. Firstly, circRNA expression profiles are affected by ionizing radiation. Studies showed that circRNA expression levels could be up-regulated or down-regulated by γ-ray, X-ray, and radon in human normal cell lines and tumor cell lines, as well as mice tissue^{28, 31, 32}. Secondly, circRNA networks can alter the radiosensitization or radioresistance status of tumor cells via microRNA sponging. For instance, down-regulating circPITX1 inhibited glycolysis to accelerate the radiosensitivity of glioma cells via sponging miR-329-3p³³. Du et al. found that circ-ZNF609 was highly expressed in prostate cancer tissues. It led to promoting the radioresistance of prostate cancer through sponging miR-501-3p³⁴. CircATRNL1 expression decreased in oral squamous cell carcinoma patients. Overexpression of circATRNL1 improved the radiosensitivity of oral squamous cell carcinoma cells by sponging miR-23a-3p, inducing apoptosis and cell-cycle arrest³⁵. Many studies have confirmed that circRNAs participate in multiple biological pathways to regulate the biology effects induced by ionizing radiation. It suggests that circRNAs have the potential as the radiation biomarkers.

Why do circRNAs suit for being biomarkers? The remarkable feature of circRNAs is their stable molecular structure. As mentioned above, circRNAs are RNase R resistant RNAs, which be attributed to their covalent close loop molecular structure. Both ecircRNAs and ciRNAs are more tolerant to RNase R treatment compared with linear RNAs⁹. The findings from this study also proved it. Therefore, the half-time of circRNAs is longer than that of linear RNAs. Enuka et al. calculated the half-time of 60 circRNAs and their linear transcripts from the same host genes. They found that the median half-time of circRNAs (18.8–23.7 h) in mammary cells was at least 2.5 times longer than that of the corresponding linear transcripts (4.0–7.4 h)¹⁰. The half-lives of circHIPK3, circKIAA0182, circASXL1, and circLPAR1 exceeded 48h, while their linear counterparts showed less than 20 h¹¹. Another study reported that circRNAs exhibited prolonged induction in irradiated mouse brain tissue and blood¹⁷. In this study, the up-regulation times of 6 circRNAs out of 11 circRNA candidates (hsa_circPCMTD1_004, hsa_circFBXW7_009, hsa_circZDHHC21_004, hsa_circBARD1_018, hsa_circFAM13B_024, and hsa_cicrPLOD2_001) at 48 h post-irradiation were higher than those of within 24 h post-irradiation. It suggested that the changes of some circRNAs were more drastic at 48 h after ionizing radiation. In the previous study published by our group, we established multiple mRNA expression dosimetry models by use of 18 radiation-responsive genes³⁶. Although these radiation-responsive genes changed in a dose-dependent manner from 6 h to 48 h post-irradiation, the fold change of expression levels for 16 mRNAs of 18 mRNA candidates at 48 h post-irradiation were lower than those for the same mRNAs within 24 h post-irradiation. We speculated that this phenomenon might attribute to the short half-lives of mRNAs. CirRNAs may be superior to mRNAs as long-term biomarkers combining the results of other research and ours. It is need extensively study to validate in near future.

Although thousands of circRNAs have been identified in eukaryotes, the abundance of most circRNAs is less than the cognate linear isoforms spliced from the same host genes. These host genes typically are active-transcriptional genes. But for some genes, their circle transcripts are highly expressed than their linear transcripts. Memczak et al. found many circRNAs in human peripheral blood were at high levels while the corresponding linear isoforms were much more lowly expressed³⁷. Another study also observed the circRNAs were much more abundant than the corresponding linear RNAs in roughly 50 genes in cell lines A549, AG04450, and HeLa¹⁵. This is because the circRNA biogenesis via back-splicing competes with the linear RNA biogenesis via canonical-splicing³⁸. The length of introns flanking circRNAs affects the circRNA generation efficiency^{7, 39}. In this study, all the mRNAs synthesized by the host genes of 11 circRNA candidates did not response to ionizing radiation in the transcriptome sequencing data (data not shown). Additionally, we observed that the Ct values of some circRNAs normalized to endogenous control genes were less than that of cognate mRNAs from the same templates in the AHH-1 cells (such as PLOD2, FBXW7, and FAM13B) (data not shown). It indicated that the pre-mRNA from some genes may prefer to choose back-splicing to form circle transcripts. Salzman et al. also found the circle transcripts from FBXW7and FAM13B were highly expressed than corresponding linear transcripts in cell lines A549, AG04450, and HeLa¹⁵. This may explain why the expression of circRNAs dependent on or responded to absorbed doses rather than the corresponding mRNAs. Ionizing radiation might induce host genes to generate more pre-mRNAs. These increased pre-mRNAs are mainly being processed to form circRNAs through back-splicing. This need further investigated in the future.

The adjusted R² values of the expression models established with multiple circRNAs in this study were greater than those of models fitted by each single circRNA at the same post-irradiation time. It indicated that dose-estimation with multiple circRNAs was superior to that with single circRNA. Other studies about dose-estimation by radiation-responsive genes also showed that it was more accuracy to estimate radiation doses with multiple genes together^{1, 36, 40}. However, the results in validation assay according to multiple expression models in this study showed that most of estimated doses had a great deviation from the actual doses (higher than 20%). We speculated that the number of circRNAs included in the expression models was still not enough. It needs further systematic study. More radiation-induced DE-circRNAs should be screened to optimize the multiple circRNA expression models. On the other hand, the radiation responsive circRNAs and radiation responsive mRNAs could be combined to establish multiple circRNA-mRNA expression models, so that the accuracy of dose-estimation will be improved.

CircRNAs expression is followed in a tissue-specific and development-specific manner. Rybak-Wolf et al. found that some circRNAs were highly enriched in synaptoneurosomes, such as circStau2a, circRims2, and so on¹⁶. Due to the character of tissue-specific expression, circRNAs may reflect the status of certain tissues or organs in physical or pathological situation. This property makes circRNAs could be disease specific biomarkers. This also suggests that whether the expression tendency of circRNAs related to specific tissues could reflect the radiation damages of certain tissues. This provides a new direction to develop partial exposure biomarkers.

The present study is mainly focusing on the radiation-induced circRNA expression changes in human lymphoblastoid cell line. In the future, it needs to confirm whether circRNAs could be radiation biomarkers in human peripheral blood exposure to ionizing radiation in vitro and in vivo. In addition, the confounding factors that affected circRNA expressions levels, such as the sex, age, health status, and lifestyle of individuals also need to be investigated.

In conclusion, radiation-induced differential expressed circRNAs were screened in human lymphoblastoid cell line AHH-1 through transcriptome sequencing. The circRNAs have the potential to be radiation biomarkers in AHH-1 cells. Because of the stability of molecular structure and specificity of expression, circRNAs have the advantage of becoming radiation biomarkers. The extensive study on whether circRNAs could be used for radiation dose estimation needs to be carried out soon.

Materials And Methods

Cell culture. The normal human lymphoblastoid cell line AHH-1 (peripheral blood B lymphocyte cell, ATCC CRL-8146) was selected to investigate the effect of ionizing radiation on the circRNA expression profile. The cells were maintained in RPMI modified medium (HyClone, SH30809.01) supplemented with 10% fetal bovine serum (Gibco, 10099-141) and 1% penicillin and streptomycin (Gibco, 15140-122). The cells were incubated at 37℃ under saturated humidity with 5% CO₂.

Cell Irradiation. The exponential growth cells were exposed to 0 (sham irradiation), 2, and 5 Gy ⁶⁰Co γ-rays at a dose-rate 1 Gy/min at room temperature. The ⁶⁰Co γ-rays source was provided by Beijing Radiation Center (Beijing, China). The distance from samples to source was 72 cm and the homogeneous irradiated area was 30 cm ×30 cm. A tissue-equivalent chamber was used to calibrate the each exposure step via physical measurements. The calibration uncertainty was 0.1%.

To investigate the relationship between ionizing radiation dose and circRNA expressions level, twenty groups of AHH-1 cells were exposed to 0, 2, 4, 6, and 8 Gy by ⁶⁰Co γ-rays (dose-rate was 1 Gy/min, same as above). The AHH-1 cells were harvested at 4h, 12h, 24h, and 48h after irradiation (five groups of samples per time point).

In the validation assay of expression models established in this study, twelve groups of AHH-1 cells were randomly numbered and induced by ⁶⁰Co γ-rays at 0, 1, and 3 Gy (dose-rate was 1 Gy/min). After irradiation, the cells were cultured at 37℃ for 4 h, 12 h, 24 h, or 48 h (three samples per time point), respectively.

Total RNA extraction and quantification. The total RNA of samples for radiation differential expression circular RNA screening was isolated by TRIzol reagent (Ambion, 15596026) at post-irradiation 4 h. The total RNA of samples for validation assay and subsequent assays was extracted by RNAprep pure Cell/ Bacteria Kit (TIANGEN China, DP430) followed the manufacturer’s protocol at 4 h and 24 h post-irradiation. All RNA sample concentrations were quantified via SMA4000 UV-VIS spectrophotometer (Merinton, USA).

Transcriptome sequencing and bioimformatic analysis. Transcriptome sequencing was done by Geneseed biotech (Guangzhou, China). The libraries were constructed via Total RNA-seq (H/M/R) Library Prep Kit for Illumina (Vazyme, China). The size of the libraries was detected by Agilent 2100 Bioanalyzer. And the library’s effective concentrations were accurately determined by Q-PCR (effective concentrations > 3 nM). The quality-controlled libraries were sequenced by double-ends sequencing. The sequencing data amount was 10 G. Triplicate was set in each group.

After sequencing, the quality of raw data from the each group was tested by FastQC software (version 0.11.9, http://www.bioinformatics.barbraham.ac.uk/projects/download.html#fastqc) firstly. The qualified raw data were filtered by use of Trimmomatic software (version 0.39, http://www.usadellab.org/cms/index.php?page=trimmomatic) to remove the adaptor and to obtain the clean reads⁴¹. For circRNA expression analysis, the reads were mapped to the human reference genome using the STAR software⁴² (version 2.7.8a, https://github.com/alexdobin/STAR). DCC software (version 0.5.0, http://github.com/dieterich-lab/DCC) was used to identify the circRNAs and to estimate the circRNA expressions⁴³. Trimmed mean of M-values was used to normalize the circRNA expressions. Differentially expressed circRNAs were identified using the edgeR program (fold change > 1.5, P < 0.05). R software was used to generate figures.

RNase R digestion. To identify the circRNAs, RNase R (GENESEED China, R0301) was adopted to digest the linear transcripts. The brief procedure is as followed. Two microgrammes of total RNA were equally divided into two parts. One part was added with RNase R and another part without adding enzyme. These two parts were incubated in a 37℃ water bath for 30 min. The digestion products were extracted by phenol-chloroform reagents.

Quantitative Real-time Polymerase Chain Reaction. To validate the reliability of results from the transcriptome sequencing, 14 candidates circRNAs from 2 Gy and 5 Gy screening were selected. The back-to-back primers used to detect circRNA expression levels were designed by the software circPimer2.0⁴⁴ (Supplementary Table S1). The total RNA of all samples was reverse-transcribed to cDNA by use of the circRNA reverse transcription kit (GENESEED China, GS0201-2) according to the manufacturer’s protocol. The circuRNA expression levels were detected by the circRNA fluorescence quantitative detection kit (GENESEED China, GS0201-1) and an Applied Biosystems 7500 Fast Real-Time PCR System (USA). U6 and GAPDH were chosen to be endogenous reference genes for normalization. The change fold of circRNA expression levels between the radiation groups and the control was calculated by the 2^−ΔΔCT method. Triplicate was performed for all the sample detection. Experiments were repeated at least three times.

Statistical analysis. All statistical analysis was performed by SPSS 22.0. The statistical data were expressed as the mean ± standard deviation. Differences in circRNA expression levels between the irradiated groups and the control were analysed by one-way ANOVA. P < 0.05 was considered as statistically significant. Dose-response relationships between each circRNA expression level and the absorbed doses were conducted by a linear regression analysis at different post-irradiated time. A stepwise regression analysis was utilized to establish the circRNA expression dosimetry models. P < 0.05 was utilized to determine which circRNA could be included or excluded in models. Collinearity diagnostics was performed to detect whether there was collinearity among variables.

Declarations

Data availability

All the transcriptomic data generated during the current study have been deposited in the GEO repository under the accessions number GSE218844 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE218844). Other datasets generated and analysed during the current study are available from the corresponding author on reasonable request.

Acknowledgements

All authors wish to thank Dr. Ling Gao for her important suggestions. This work was funded by the National Natural Science Foundation of China (No. 82173463 to Q. J. L.).

Author contributions

X.L.T. and Q.J.L. designed the study. T.T.Z. and T.J.C. performed the cell culture and cell irradiation. X.L.T. preformed all the molecular biology assays, the data collection, and the data analysis.X.L.T. drafted the manuscript. Q.J.L. and M.T. commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Funding

This work was funded by the National Natural Science Foundation of China (No. 82173463 to Q. J. L.).

Competing interests

The authors declare no competing interests.

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