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. 2017 Mar 17;7:44132. doi: 10.1038/srep44132

Changes in miRNA in the lung and whole blood after whole thorax irradiation in rats

Feng Gao 1, Pengyuan Liu 2, Jayashree Narayanan 1, Meiying Yang 3, Brian L Fish 1, Yong Liu 2, Mingyu Liang 2, Elizabeth R Jacobs 4,5, Meetha Medhora 1,4,5,a
PMCID: PMC5355888  PMID: 28303893

Abstract

We used a rat model of whole thorax x-ray irradiation to profile the microRNA (miRNA) in lung and blood up to 4 weeks after radiation. MiRNA from normal and irradiated Wistar rat lungs and whole blood were analyzed by next-generation sequencing and the changes by radiation were identified by differential deRNA-seq 1, 2, 3 and 4 weeks after irradiation. The average total reads/library was 2,703,137 with a mean of 88% mapping to the rat genome. Detailed profiles of 100 of the most abundant miRNA in rat blood and lung are described. We identified upregulation of 4 miRNA, miR-144-5p, miR-144-3p, miR-142-5p and miR-19a-3p in rat blood 2 weeks after radiation that have not previously been shown to be altered after radiation to the lung. Ingenuity Pathway Analysis identified signaling of inflammatory response pathways. These findings will support development of early detection methods, as well as mechanism(s) of injury and mitigation in patients after radiotherapy or radiological accidents.

Radiation lung injuries

Radiation-induced lung injury is characterized by acute pneumonitis and chronic fibrosis1,2,3 both of which can be lethal. Acute pneumonitis in humans develops within the first 2 to 3 months after irradiation, while chronic pulmonary fibrosis manifests months or even years later3. We have developed a rat model of whole thoracic radiation by X-rays to induce pneumonitis from 6–12 weeks post exposure and pulmonary fibrosis after 30 weeks4,5. We and others showed early treatment with mitigators like angiotensin converting enzyme (ACE) inhibitors, enhances survival and improves lung function after radiation6,7,8,9,10. In fact the ACE inhibitor enalapril can be started 5 weeks after radiation to mitigate pneumonitis and fibrosis9. However, in the event of a radiological accident or attack, it will remain challenging to determine who to treat since accurate dosimetry may not be possible. In addition, sensitivity to radiation may vary between people. Therefore development of biomarkers to predict injuries after radiation but before symptoms develop has become an important area of research.

Changes in miRNA associated with irradiated lungs

Circulating miRNA biomarkers have been reported in many diseases including those involving the lungs11. Since miRNA in circulating blood is considered to be a non-invasive measurement and can be an indication of specific disease conditions, circulating miRNA may be considered for development of biomarkers. Changes in miRNA after radiation have been reported in lung cancer patients undergoing radiotherapy in the clinic12. But, analysis of miRNA changes that may occur after a radiological accident or terrorism attack is not feasible in humans. Information about miRNA changes in the lung after radiation will facilitate a better understanding of the mechanism(s) of injury as well as identify molecular targets for therapy. Animal models have been used for such studies13,14. In a mouse model, Jacob et al. identified differentially expressed serum miRNAs 24–72 hours after total body irradiation13. MiRNA expression was studied using the nanostring nCounter multiplex platform, which is capable of detecting approximately 600 mouse specific miRNAs. Recently Xie et al. reported lung miRNA expression in response to radiation-induced lung injury in rats. Lung miRNA was evaluated with microarrays (387 miRNAs) at 3, 12 and 26 weeks. However, miRNA profiles from blood or tissues that can be obtained by minimally invasive methods (such as body fluids) were not investigated in this study14.

Therefore the aims of the current project were: (1) to provide comprehensive profiles of the lung and blood miRNA in rats by next-generation sequencing to detect molecular changes by radiation; (2) to look for circulating miRNAs in whole blood to develop candidate biomarkers for radiation injuries. MiRNA changes were analyzed weekly up to 4 weeks after radiation, which is one week before we can intervene to mitigate pneumonitis with an ACE inhibitor9, and months before pulmonary fibrosis develops.

Results

Total profile of miRNA from lung and blood

Total RNA was used to generate small RNA libraries (n = 36) by size selecting for miRNA (Table 1 and Methods). Each library contained miRNA pooled from 3 rats and a total of 3 libraries were analyzed for each time point 1, 2, 3 and 4 weeks after irradiation. Separate libraries were prepared from blood and lung, each represented by 9 rats/time point. The average total reads/library was 2,703,137. The average total unique reads/library was 54,268. The percent reads that mapped to the rat genome15 ranged from 64.7 to 98.9%, with a mean of 88%. Detailed data are shown in Table 1. A total of 947 miRNAs were identified by the next generation sequencing and 458 of them were mature unique miRNAs that have been identified in rats while 116 were homologous unique miRNAs that have been identified in other species. Another 373 miRNA were considered as new miRNA (Supplemental Table 1) that have not been previously described.

Table 1. 36 libraries (18 from lung and 18 from blood) were generated.

Library # Tissue Radiation Timepoint total unique reads total reads unmapped unique reads unmapped reads %mapped reads
1 Blood 0 Gy 1 week 27983 4263244 7279 47509 98.9
2 Blood 0 Gy 1 week 38512 3443368 10356 52807 98.5
3 Blood 0 Gy 1 week 30772 4415596 8186 59870 98.6
4 Blood 0 Gy 4 weeks 32410 4019965 8521 54521 98.6
5 Blood 0 Gy 4 weeks 34329 4607361 9001 64078 98.6
6 Blood 0 Gy 4 weeks 50163 4228658 12766 77878 98.2
7 Blood 15 Gy 1 week 33005 4188089 8374 50762 98.8
8 Blood 15 Gy 1 week 34273 3477659 8872 52756 98.5
9 Blood 15 Gy 1 week 38374 3784442 9743 51171 98.6
10 Blood 15 Gy 2 weeks 32833 2257353 7819 38353 98.3
11 Blood 15 Gy 2 weeks 39243 3458791 9858 71994 97.9
12 Blood 15 Gy 2 weeks 40031 3490172 10290 74264 97.9
13 Blood 15 Gy 3 weeks 24835 3917978 6714 43091 98.9
14 Blood 15 Gy 3 weeks 37330 4006866 9804 70381 98.2
15 Blood 15 Gy 3 weeks 32440 3533270 8361 45112 98.7
16 Blood 15 Gy 4 weeks 34195 3265611 9001 65210 98.0
17 Blood 15 Gy 4 weeks 42711 3665273 11068 62775 98.3
18 Blood 15 Gy 4 weeks 25427 3361941 6713 35690 98.9
19 Lung 0 Gy 1 week 87819 2066852 20810 470429 77.2
20 Lung 0 Gy 1 week 68449 1574877 16564 501396 68.2
21 Lung 0 Gy 1 week 4796 25166 1102 4535 82.0
22 Lung 0 Gy 4 weeks 147735 4586020 36274 803019 82.5
23 Lung 0 Gy 4 weeks 138509 3087369 34716 803220 74.0
24 Lung 0 Gy 4 weeks 71645 1882795 17737 219969 88.3
25 Lung 15 Gy 1 week 99936 2051176 24196 356010 82.6
26 Lung 15 Gy 1 week 40336 653589 11873 223218 65.8
27 Lung 15 Gy 1 week 96498 2195629 22424 290730 86.8
28 Lung 15 Gy 2 weeks 95576 1739850 26142 153659 91.2
29 Lung 15 Gy 2 weeks 61195 1175767 16564 300830 74.4
30 Lung 15 Gy 2 weeks 33465 496463 9820 175151 64.7
31 Lung 15 Gy 3 weeks 93148 1536333 25929 413899 73.1
32 Lung 15 Gy 3 weeks 82228 1883847 19744 295489 84.3
33 Lung 15 Gy 3 weeks 4076 23541 1536 7505 68.1
34 Lung 15 Gy 4 weeks 106685 3150602 26561 804574 74.5
35 Lung 15 Gy 4 weeks 62832 1379394 17365 406278 70.5
36 Lung 15 Gy 4 weeks 29857 418039 9462 123817 70.4
Mean 54268 2703137 13932 204776 88
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Each library contained microRNA from 3 rats and 3 libraries were analyzed at each time point, representing 9 rats/time point. Read count and mapping rate to the rat genome of each library are shown.

We chose to follow abundantly expressed miRNAs that had more than ~100 reads in blood and ~1000 reads in lung of the control rats (Table 2). With these thresholds, we identified 113 most expressed miRNAs in blood and lung of control rats (0 Gy) (Table 2). Among these miRNA, 43 of them from blood and 47 of them from lung were identified as new miRNAs (Table 2). In both lung and blood, 57 of the top 113 detected miRNAs were mature unique miRNAs (Fig. 1). Top 20 miRNA from lung and blood are shown in Table 3 along with results from other studies. The results from bovine blood16 were derived by a similar protocol as the rat blood, miRNA-seq. The rat lung miRNA in the previous study17 was obtained from a microarray and not by sequencing and so could be expected to show differing results. Since there was a large fold difference in expression of rno-miR-486-5p and 451-5p in rat blood but not bovine blood, we carried out RT-qPCR for a sub-set of the top 10 miRNA in whole rat blood without amplification or any additional experimental intervention. Amplification has the potential to alter ratios of expressed miRNA during library preparation. Expression of miR-486-5p by RT-qPCR was in the same range as miR-451-5p suggesting reads for miR-486-5p were detected at a much higher ratio by miRNA-seq in rat blood. The expression of miR-486-5p by RT-qPCR was found to be 9.5 fold higher than that of miR-191-5p in rats. Though miR-16-5p was not among the top 10 miRNA in bovine blood16 but is abundant in human erythrocytes17, we checked expression by RT-qPCR and found it was also in the same range as miR-451-5p and 486-5p.

Table 2. The 113 most highly expressed microRNAs in blood and lung of control rats (0 Gy) were listed.

Most highly expressed (top 113) miRNA
rat blood category read counts (n = 6) % top 113 rat lung category read counts (n = 6) % top 113
rno-miR-486-5p mature_unique 1940840 92.566 chr18_57625094_57625241 newMicroRNA_genome 207773 12.896
rno-miR-92a-3p mature_unique 23299 1.111 chr18_57625124_57625204 newMicroRNA_fastgenome 207425 12.875
rno-miR-191a-5p mature_unique 21565 1.029 rno-miR-143-3p mature_unique 174207 10.813
rno-miR-16-5p mature_unique 9957 0.475 rno-miR-10a-5p mature_unique 84796 5.263
chr8_123981613_123981719 newMicroRNA_homolog 7772 0.371 chr8_123981613_123981719 newMicroRNA_homolog 42502 2.638
bta-miR-26c maturehomolog_unique 7370 0.351 chr8_123981623_123981705 newMicroRNA_fasthomolog 42468 2.636
rno-miR-3596a mature_unique 7150 0.341 bta-miR-26c maturehomolog_unique 41087 2.550
rno-miR-25-3p mature_unique 6881 0.328 rno-miR-26a-5p mature_unique 36273 2.251
rno-miR-26a-5p mature_unique 6610 0.315 rno-miR-30a-5p mature_unique 35274 2.189
rno-miR-451-5p mature_unique 4558 0.217 rno-miR-3596a mature_unique 32088 1.992
rno-let-7i-5p mature_unique 3874 0.185 chr1_56486962_56487048 newMicroRNA_genome 31215 1.937
rno-miR-181a-5p mature_unique 3794 0.181 chr9_22142880_22142968 newMicroRNA_fasthomolog 31181 1.935
rno-let-7f-5p mature_unique 3021 0.144 chr9_22142869_22142978 newMicroRNA_homolog 31181 1.935
chr18_8923954_8924050 newMicroRNA_genome 2936 0.140 chr1_56486961_56487047 newMicroRNA_fastgenome 29074 1.805
rno-let-7c-5p mature_unique 2780 0.133 rno-miR-99b-5p mature_unique 28132 1.746
chr17_7351428_7351565 newMicroRNA_genome 2508 0.120 rno-miR-486-5p mature_unique 27940 1.734
chrX_139740786_139740910 newMicroRNA_genome 2394 0.114 rno-let-7f-5p mature_unique 24028 1.491
rno-miR-151-3p mature_unique 2393 0.114 rno-miR-126a-5p mature_unique 22723 1.410
rno-miR-27b-3p mature_unique 2224 0.106 rno-miR-181a-5p mature_unique 20558 1.276
rno-let-7a-5p mature_unique 1291 0.062 chr1_251558040_251558146 newMicroRNA_genome 19452 1.207
chr9_22142880_22142968 newMicroRNA_fasthomolog 1123 0.054 chr1_251558049_251558132 newMicroRNA_fastgenome 19452 1.207
chr9_22142869_22142978 newMicroRNA_homolog 1123 0.054 bta-miR-3600 maturehomolog_unique 18827 1.169
rno-let-7b-5p mature_unique 1112 0.053 chr10_62782697_62782840 newMicroRNA_homolog 18640 1.157
bta-miR-3596 maturehomolog_unique 1071 0.051 chr10_62782728_62782812 newMicroRNA_fasthomolog 18623 1.156
rno-miR-30a-5p mature_unique 1041 0.050 rno-miR-146b-5p mature_unique 17794 1.104
chr19_37422692_37422817 newMicroRNA_genome 979 0.047 chr1_56487554_56487694 newMicroRNA_genome 17044 1.058
chr19_37422714_37422799 newMicroRNA_fastgenome 979 0.047 rno-miR-22-3p mature_unique 16162 1.003
rno-miR-140-3p mature_unique 876 0.042 rno-miR-125a-5p mature_unique 15567 0.966
rno-miR-186-5p mature_unique 812 0.039 rno-miR-30d-5p mature_unique 13955 0.866
rno-let-7d-5p mature_unique 809 0.039 rno-miR-191a-5p mature_unique 12568 0.780
rno-miR-3596b mature_unique 801 0.038 chr17_7351428_7351565 newMicroRNA_genome 11758 0.730
rno-miR-93-5p mature_unique 770 0.037 rno-miR-126a-3p mature_unique 11732 0.728
hsa-miR-148a-3p maturehomolog_unique 657 0.031 chr17_7351451_7351539 newMicroRNA_fastgenome 11136 0.691
rno-miR-30e-5p mature_unique 642 0.031 chr1_56487580_56487664 newMicroRNA_fastgenome 11053 0.686
bta-miR-3600 maturehomolog_unique 621 0.030 bta-miR-2340 maturehomolog_unique 11037 0.685
chr10_62782697_62782840 newMicroRNA_homolog 613 0.029 rno-miR-27b-3p mature_unique 10674 0.662
chr17_7351451_7351539 newMicroRNA_fastgenome 542 0.026 rno-miR-92a-3p mature_unique 9513 0.590
rno-miR-22-3p mature_unique 538 0.026 chr7_105819723_105819875 newMicroRNA_genome 9073 0.563
rno-miR-30d-5p mature_unique 525 0.025 rno-miR-151-3p mature_unique 8838 0.549
chr7_105819723_105819875 newMicroRNA_genome 497 0.024 chr9_74233487_74233589 newMicroRNA_genome 7084 0.440
rno-miR-15b-5p mature_unique 471 0.022 rno-let-7i-5p mature_unique 6882 0.427
chr10_67078689_67078839 newMicroRNA_homolog 453 0.022 rno-miR-375-3p mature_unique 6440 0.400
chr10_67078715_67078809 newMicroRNA_fasthomolog 453 0.022 rno-miR-92b-3p mature_unique 6216 0.386
hsa-miR-3184-3p maturehomolog_unique 447 0.021 rno-miR-182 mature_unique 5300 0.329
rno-miR-30c-5p mature_unique 438 0.021 rno-let-7a-5p mature_unique 5173 0.321
rno-miR-423-5p mature_unique 412 0.020 rno-miR-351-5p mature_unique 5141 0.319
hsa-miR-103b maturehomolog_unique 404 0.019 rno-let-7c-5p mature_unique 5023 0.312
rno-miR-423-3p mature_unique 398 0.019 rno-miR-28-3p mature_unique 4798 0.298
rno-miR-378a-3p mature_unique 391 0.019 chr2_181395394_181395545 newMicroRNA_genome 3728 0.231
chr3_118996576_118996714 newMicroRNA_homolog 389 0.019 rno-miR-30c-5p mature_unique 3643 0.226
chr10_20694998_20695142 newMicroRNA_homolog 389 0.019 rno-miR-16-5p mature_unique 3389 0.210
chr19_25667395_25667479 newMicroRNA_fastgenome 389 0.019 chr2_181395424_181395508 newMicroRNA_fastgenome 3346 0.208
rno-miR-320-3p mature_unique 372 0.018 chr19_25638547_25638840 newMicroRNA_genome 3206 0.199
chr15_50842920_50843068 newMicroRNA_genome 358 0.017 chr8_44518661_44518763 newMicroRNA_homolog 2946 0.183
rno-miR-21-5p mature_unique 341 0.016 rno-miR-125b-5p mature_unique 2753 0.171
chr10_74864475_74864611 newMicroRNA_genome 333 0.016 rno-miR-3588 mature_unique 2703 0.168
chr10_76049262_76049384 newMicroRNA_genome 325 0.015 chr11_78139638_78139790 newMicroRNA_homolog 2644 0.164
chr10_76049282_76049366 newMicroRNA_fastgenome 320 0.015 chr11_78139668_78139752 newMicroRNA_fasthomolog 2644 0.164
chr5_137468994_137469092 newMicroRNA_genome 319 0.015 ppy-miR-151b maturehomolog_unique 2623 0.163
rno-miR-181c-5p mature_unique 305 0.015 rno-miR-100-5p mature_unique 2588 0.161
bta-miR-2340 maturehomolog_unique 304 0.014 rno-miR-3586-3p mature_unique 2572 0.160
rno-miR-106b-3p mature_unique 300 0.014 hsa-miR-148a-3p maturehomolog_unique 2551 0.158
rno-miR-3557-5p mature_unique 300 0.014 chr19_25638578_25638661 newMicroRNA_fastgenome 2535 0.157
chr12_17608366_17608479 newMicroRNA_genome 291 0.014 chr5_172897165_172897291 newMicroRNA_genome 2432 0.151
chr12_17608379_17608463 newMicroRNA_fastgenome 291 0.014 rno-miR-151-5p mature_unique 2427 0.151
rno-miR-103-3p mature_unique 288 0.014 rno-miR-429 mature_unique 2358 0.146
chr3_18651980_18652124 newMicroRNA_genome 264 0.013 rno-miR-141-3p mature_unique 2308 0.143
rno-miR-143-3p mature_unique 243 0.012 chr1_95595998_95596190 newMicroRNA_genome 2214 0.137
rno-miR-181b-5p mature_unique 243 0.012 chr1_95596079_95596160 newMicroRNA_fastgenome 2214 0.137
chr15_99853725_99853834 newMicroRNA_genome 234 0.011 rno-miR-27a-3p mature_unique 2197 0.136
rno-miR-328a-3p mature_unique 226 0.011 rno-miR-146a-5p mature_unique 2142 0.133
rno-miR-17-5p mature_unique 225 0.011 rno-miR-150-5p mature_unique 2075 0.129
cgr-miR-1285 maturehomolog_unique 222 0.011 chr9_73734035_73734173 newMicroRNA_genome 1968 0.122
chr2_255655012_255655108 newMicroRNA_genome 221 0.011 chr11_16397521_16397651 newMicroRNA_homolog 1963 0.122
rno-miR-192-5p mature_unique 210 0.010 chr11_78139643_78139779 newMicroRNA_genome 1940 0.120
chrX_139741405_139741553 newMicroRNA_genome 208 0.010 chr11_78139668_78139752 newMicroRNA_fastgenome 1937 0.120
hsa-miR-3184-5p maturehomolog_unique 205 0.010 chr13_77910750_77910898 newMicroRNA_homolog 1889 0.117
chr1_209040238_209040380 newMicroRNA_genome 192 0.009 chr13_77910785_77910868 newMicroRNA_fasthomolog 1889 0.117
chr18_57625094_57625241 newMicroRNA_genome 188 0.009 rno-miR-99a-5p mature_unique 1817 0.113
bta-miR-2487 maturehomolog_unique 187 0.009 rno-miR-29a-3p mature_unique 1789 0.111
rno-let-7d-3p mature_unique 182 0.009 chr9_22142881_22142971 newMicroRNA_fastgenome 1760 0.109
chr19_25667542_25667672 newMicroRNA_genome 179 0.009 rno-miR-378a-3p mature_unique 1752 0.109
chr1_251558040_251558146 newMicroRNA_genome 177 0.008 rno-miR-24-3p mature_unique 1750 0.109
rno-miR-146b-5p mature_unique 175 0.008 rno-miR-26b-5p mature_unique 1743 0.108
bta-miR-2898 maturehomolog_unique 169 0.008 rno-miR-21-5p mature_unique 1699 0.105
chr1_95595998_95596190 newMicroRNA_genome 168 0.008 bta-miR-3604 maturehomolog_unique 1692 0.105
rno-miR-142-5p mature_unique 168 0.008 chr10_74864475_74864611 newMicroRNA_genome 1682 0.104
rno-miR-181d-5p mature_unique 166 0.008 rno-let-7b-5p mature_unique 1671 0.104
chr1_95596079_95596160 newMicroRNA_fastgenome 164 0.008 chr9_22142873_22142980 newMicroRNA_genome 1663 0.103
rno-miR-148b-3p mature_unique 164 0.008 rno-miR-200b-3p mature_unique 1616 0.100
rno-miR-3586-3p mature_unique 162 0.008 chr5_172898998_172899090 newMicroRNA_genome 1609 0.100
rno-miR-144-5p mature_unique 161 0.008 rno-miR-186-5p mature_unique 1563 0.097
rno-miR-99b-5p mature_unique 161 0.008 rno-miR-25-3p mature_unique 1555 0.097
rno-miR-128-3p mature_unique 161 0.008 rno-miR-30e-5p mature_unique 1546 0.096
rno-miR-150-5p mature_unique 159 0.008 rno-miR-101a-3p mature_unique 1450 0.090
rno-miR-151-5p mature_unique 153 0.007 rno-miR-192-5p mature_unique 1434 0.089
chr8_116727221_116727334 newMicroRNA_genome 150 0.007 chr3_57340837_57340983 newMicroRNA_genome 1427 0.089
rno-miR-144-3p mature_unique 149 0.007 chr3_57340843_57340973 newMicroRNA_homolog 1403 0.087
rno-miR-26b-5p mature_unique 147 0.007 rno-miR-199a-3p mature_unique 1381 0.086
rno-miR-425-5p mature_unique 142 0.007 rno-miR-34c-5p mature_unique 1319 0.082
chr19_35122527_35122673 newMicroRNA_genome 134 0.006 chr8_54422016_54422156 newMicroRNA_genome 1279 0.079
chr1_56486961_56487047 newMicroRNA_fastgenome 132 0.006 chr8_54422050_54422129 newMicroRNA_fastgenome 1277 0.079
rno-miR-10a-5p mature_unique 131 0.006 chr1_209040238_209040380 newMicroRNA_genome 1256 0.078
chr10_62782728_62782812 newMicroRNA_fasthomolog 129 0.006 chr3_18651980_18652124 newMicroRNA_genome 1249 0.078
hsa-miR-1303#ptr-miR-1303 maturehomolog_unique 128 0.006 rno-miR-181b-5p mature_unique 1183 0.073
chr1_219153600_219153686 newMicroRNA_genome 127 0.006 chr10_67078689_67078839 newMicroRNA_homolog 1163 0.072
chrX_35821301_35821427 newMicroRNA_genome 123 0.006 chr10_67078715_67078809 newMicroRNA_fasthomolog 1158 0.072
rno-miR-652-3p mature_unique 122 0.006 rno-miR-3557-5p mature_unique 1156 0.072
chr8_113614814_113614902 newMicroRNA_fastgenome 112 0.005 rno-miR-423-3p mature_unique 1150 0.071
chr8_113614807_113614909 newMicroRNA_genome 112 0.005 rno-miR-127-3p mature_unique 1133 0.070
chr3_118996605_118996685 newMicroRNA_fasthomolog 112 0.005 rno-miR-23b-3p mature_unique 1052 0.065
rno-miR-101a-3p mature_unique 102 0.005 rno-let-7e-5p mature_unique 1020 0.063
rno-miR-484 mature_unique 97 0.005 chr5_172899003_172899082 newMicroRNA_fastgenome 1019 0.063
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These microRNAs showed more than 100 and ~1000 reads in blood and lung of the control rats respectively. Read count and percentage of the reads of individual microRNA in the sum of top 113 are also provided.

Figure 1.

Figure 1

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The top 57 most abundant mature miRNAs in the blood (a) and lung (b) of normal rats.

Table 3. A comparison of the profile of the most abundant microRNA in blood and lung found in the current study compared to those of Spornraft et al. and Caruso et al.

Most highly expressed (top20) miRNA
Current study Previous studies 16,17
rat blood read counts (n = 6) % top 20 rat lung read counts (n = 6) % top 20 bovine blood rpm16 (n = 9) % top 10 male rat lung absolute levels17(n = 5) % top 20
rno-miR-486-5p 1940840 94.91 rno-miR-143-3p 174207 28.57 bta-miR-451 74917 27.5 rno-miR-26a 11526 10.1
rno-miR-92a-3p 23299 1.14 rno-miR-10a-5p 84796 13.91 bta-miR-486 58400 21.4 rno-miR-126 11056 9.7
rno-miR-191a-5p 21565 1.05 rno-miR-26a-5p 36273 5.95 bta-miR-25 40788 15 rno-let-7a 9747 8.5
rno-miR-16-5p 9957 0.49 rno-miR-30a-5p 35274 5.79 bta-miR-92a 38089 14 rno-let-7c 8762 7.7
rno-miR-3596a 7150 0.35 rno-miR-3596a 32088 5.26 bta-miR-191 13807 5.1 rno-let-7f 8290 7.2
rno-miR-25-3p 6881 0.34 rno-miR-99b-5p 28132 4.61 bta-let-7i 11134 4.1 rno-let-7b 7268 6.4
rno-miR-26a-5p 6610 0.32 rno-miR-486-5p 27940 4.58 bta-miR-185 9136 3.4 rno-miR-23b 6669 5.8
rno-miR-451-5p 4558 0.22 rno-let-7f-5p 24028 3.94 bta-miR-339a 8965 3.3 rno-miR-23a 6618 5.8
rno-let-7i-5p 3874 0.19 rno-miR-126a-5p 22723 3.73 bta-miR-26a 8823 3.2 rno-let-7d 6525 5.7
rno-miR-181a-5p 3794 0.19 rno-miR-181a-5p 20558 3.37 bta-miR-21-5p 8364 3.1 rno-miR-145 4950 4.3
rno-let-7f-5p 3021 0.15 rno-miR-146b-5p 17794 2.92       rno-let-7i 4293 3.8
rno-let-7c-5p 2780 0.14 rno-miR-22-3p 16162 2.65       rno-miR-30c 4085 3.6
rno-miR-151-3p 2393 0.12 rno-miR-125a-5p 15567 2.55       rno-miR-16 4051 3.5
rno-miR-27b-3p 2224 0.11 rno-miR-30d-5p 13955 2.29       rno-miR-30b-5p 3716 3.2
rno-let-7a-5p 1291 0.06 rno-miR-191a-5p 12568 2.06       rno-miR-125b-5p 3117 2.7
rno-let-7b-5p 1112 0.05 rno-miR-126a-3p 11732 1.92       rno-miR-195 3062 2.7
rno-miR-30a-5p 1041 0.05 rno-miR-27b-3p 10674 1.75       rno-let-7e 2978 2.6
rno-miR-140-3p 876 0.04 rno-miR-92a-3p 9513 1.56       rno-miR-24 2724 2.4
rno-miR-186-5p 812 0.04 rno-miR-151-3p 8838 1.45       rno-miR-26b 2709 2.4
rno-let-7d-5p 809 0.04 rno-let-7i-5p 6882 1.13       rno-miR-29a 2271 2.0
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In the study of Spornraft et al.16, read counts were presented in reads per million (rpm) and n = 9; in the study of Caruso et al.17, counts were presented as absolute levels and n = 5; in the current study, total read counts were shown and n is 6 libraries, each of which included pooled RNA from 3 rats.

Changes in miRNA profile by radiation

The numbers of the miRNA changed after radiation in blood and lung at different time points from 1 week to 4 weeks are shown in Table 4. The total reads we identified in the lung were more than those in the blood. At 1, 2 and 3 weeks the lung was found to have more miRNAs that were changed by radiation than blood (62 vs. 2; 24 vs. 13; 22 vs. 5 increase at 1, 2 and 3 weeks respectively; 40 vs. 3; 16 vs. 3; 64 vs. 15 decrease at 1, 2 and 3 weeks respectively). However, numbers of miRNA changed at 4 weeks after radiation were similar in lung and blood (4 vs. 5 increase and 8 vs. 10 decrease). Some miRNA remained changed at more than one time-point after radiation. The numbers of the changes with overlaps at different time points of the same miRNA are also listed in the Table 4. The most overlap of the upregulated miRNA in lung was at 1 and 2 weeks, with an overlap of 11 miRNAs, while 10 miRNAs at 2 weeks and 11 at 3 weeks were also decreased at 1 week.

Table 4. Number of microRNA changed after radiation.

# of miRNA changed after radiation (p < 0.05)
  ↑ ↓
1w 2w 3w 4w 1w 2w 3w 4w
Lung 1w 62 11 5   40 10 11 1
2w 11 24 1 1 10 16 6 1
3w 5 1 22 2 11 6 64 5
4w   1 2 4 1 1 5 8
Blood 1w 2 1     3 1 1 1
2w 1 13     1 3 1 1
3w     5 3 1 1 15 7
4w     3 5 1 1 7 10
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↑ : increase after radiation; ↓ : decrease after radiation. w: week. p < 0.05 radiated vs. non-irradiated control. Egs: 62 microRNAs were found to be increased 1week after radiation in lung; 11 of the 62 were also increased at 2 weeks after radiation in lung, 5 of the 62 were also increased 3 weeks after radiation in lung. 24 microRNAs were found to be increased in lung at 2 weeks after radiation, of them 11 was also increased at 1week, as mentioned above, and 1 microRNA was also increased at 3 weeks, and 1 also increased at 4 weeks.

Identification of circulating miRNA altered by radiation

Since the most miRNA changes after radiation in whole blood were shown to be at 2 weeks (13 increased) and 3 weeks (15 decreased) (Table 4), we focused on the 2-week time point to develop circulating miRNA markers. MiRNAs with statistical significance based on read counts from the blood libraries (p < 0.05 between 15 Gy at 2 weeks and 0 Gy at 1 week on the reads) were further tested by RT-qPCR (Fig. 2a). We focused on miRNA that increased after radiation since a decrease in miRNA may reflect the fall in circulating blood cells at 2 weeks. We verified the up regulation of these 4 miRNAs, which are miR-142-5p, 144-3p, 144-5p and 19a-3p by radiation (Fig. 2a). We also tested miRNAs by RT-qPCR that were reported by others to change after radiation13 and confirmed miR-150-5p was down regulated and miR-21-5p18 was up regulated by radiation (Fig. 2b). Sequencing results of these two miRNAs in two week blood samples followed the same trend as the RT-qPCR, though the results did not reach statistical significance between the read counts from non-irradiated controls (0 Gy) versus 15 Gy. The read counts are given in Fig. 2c. All the miRNA that we verified to be changed by RT-qPCR at 2 weeks after radiation in blood were further tested in the lung tissue at 2 weeks. Except 150-5p which showed the same change (decreased after radiation), none of them were changed in lung (data not shown). Other miRNAs described in the literature, but which did not show changes in our rat model in blood between controls and radiated rats at 2 weeks were 99b-5p, 30a-5p, let7-5p 9a-5p and 92b-3p (data not shown). The results of the RT-qPCR on these miRNAs matched our findings from sequencing.

Figure 2. Relative miRNA expression after 2 weeks post-radiation in blood, as determined by RT-qPCR.

Figure 2

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The line in each box represents the median value. The box spans the 25th and 75th percentiles. The upper and lower bars show the maximum and minimum values. N = 11 rats/box. *P < 0.05 radiated vs. control (0 Gy). MiR-191-5p was used as reference because it shows consistent read counts in all sequenced libraries. miRNA in (a) were selected by results of miRNA-seq for candidates with p < 0.05 radiated vs. control by t test. miRNA in (b) were selected because other investigators previously identified these to be altered after radiation or lung injury. Their reads count with p values from the sequence results of this study are shown in (c). # refers to the library number # in Table 1.

Changes in circulating blood cells after whole thorax irradiation

The WBC are lower after one week while RBC fall by 2 weeks after radiation (Fig. 3). The WBC recover by 4 weeks.

Figure 3.

Figure 3

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Blood cell counts versus time are shown (a) White blood cell count (WBC) and (b) Red blood cell count (RBC). *p < 0.05 vs. control (0 Gy). N = 14 (controls), 6 (irradiated at 1 week), 7 (irradiated at 2 weeks), 9 (irradiated at 3 weeks), 9 (irradiated at 4 weeks). The line represents the median.

Pathway analysis of miRNA changes in blood

The changes observed in the 2 week sequencing data from blood were also used to explore signaling pathways that are altered by radiation using the Ingenuity Pathway Analysis platform. The results showed that the top diseases and functional pathways that can be associated with the blood at 2 weeks after radiation were inflammatory disease, inflammatory response and connective tissue disorders (Fig. 4).

Figure 4. Ingenuity pathway analysis of the sequence data showed the top scoring diseases and function that are associated with our model (upper panel).

Figure 4

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The pathway map is presented below and miRNA that were significantly changed in the miRNA-seq study and also validated by qPCR are shown as highlighted in red.

Discussion

In this study, we report the miRNA profile obtained by RNA-seq from rat lung and blood at baseline and 1, 2, 3 and 4 weeks after 15 Gy whole thorax irradiation. This dose induces lethal pneumonitis in rats after 42 days, though all rats survive 12 Gy whole thorax irradiation19. Doses of 13 or 14 Gy induce intermediate levels of lethality19. We used RT-qPCR to verify changes in levels of at least 6 circulating miRNA that were identified by RNA-seq at 2 weeks after 15 Gy. At this time point all rats are healthy with normal breathing rates and lung histology though we have measured an increase in vascular permeability and apoptosis in the radiated lungs20. Some of the miRNA that were altered in the circulation e.g. miRs 142-5p, 150-5p and 21-5p have been described in anti-apoptotic responses and may signify cellular compensation for increased hematopoietic death induced by radiation21,22,23,24.

The miRNA molecules we sequenced were sized by gel purification to exclude RNA other than miRNA. Our results show miRNA-seq to be a sensitive technique, yielding a dynamic range of read counts from 1 − 1.9 × 106 (Supplemental Table 1). Additionally 88 and 98% mean reads mapped to the rat genome in the lung and blood samples respectively (Table 1). Unmapped reads could occur by sequencing errors, artifacts during PCR amplification, short reads, trimming or repetitive DNA sequences etc. Most changes in miRNA were observed at 1 week after radiation in lung tissue. MiRNA changes at earlier time points have been evaluated in other models of radiation but not with exhaustive techniques such as miRNA-seq.

High-throughput technology such as miRNA-seq has many advantages including generation of large data sets which reflect the biology of the system. However, our study, in which expression of miR-486-5p may be overrepresented, also demonstrates limitations. Though most of the top 10 miRNA species which we identified as expressed in whole blood corresponds with another report16, expression of miRNA of interest should be confirmed by independent techniques/platforms regardless of specific technology or methods. We validated candidate circulating miRNAs from the high throughput RNA-seq individually by RT-qPCR. Nine miRNA showing increase from control after radiation were selected for verification by RT-qPCR. Though most miRNA trended in the same direction in both assays only 4 miRNA were statistically increased after RT-qPCR.

The profile of miRNA in normal rat blood and lung (0 Gy, without radiation) that we report in this paper (Tables 2and 3, Fig. 1) and elaborated in the supplement, shows new species that fit the definition of miRNA but remain to be characterized. More than 100 miRNA from blood and 1000 from lungs of non-irradiated rats yielded read counts greater than 100 (a value that can be consistently and easily verified by RT-qPCR). Both blood and lung tissue had 57 mature miRNAs that have been previously identified in the top 113 detected (Fig. 1). Consistent with the previous findings25, high expression of miR-486-5p in red blood cells was also detected in whole rat blood. This miRNA was described to regulate normal erythropoiesis26 and was also suggested to be an important oncogene in several hematopoietic myeloid malignancies26. MiR-486-5p was reported to be a potential plasma-based biomarker for lung cancer27. Leuenberge et al. showed that its abundance remained unchanged after blood transfusion in healthy males28. Therefore, it was used as an endogenous control for data normalization in blood miRNA studies28,29. In the current study, we observed decrease in miR-486-5p read-counts in rat blood at 2 weeks after radiation. However these changes could not be confirmed by RT-qPCR (Fig. 2). This, along with verification of only 4/9 candidate miRNA by RT-qPCR reiterates the limitation of miRNA-seq.

Using next generation sequencing, we report changing miRNA patterns after radiation in rat lung and blood (Table 4). All RNA used for these comparisons were prepared at the same time with identical reagents and equipment. Such data can provide useful algorithms to develop early biomarkers for treatment plans in radiotherapy. As an adjunct diagnostic method, next generation sequencing provides abundant information. We verified an increase of miR-142-5p, 144-3p, 144-5p and 19-3p in blood after 15 Gy in our rat model. Another two interesting miRNA (miR-150-5p and 21-5p) were selected for testing by RT-qPCR based on changes after radiation as reported by others. MiR-150-5p was shown to be decreased after radiation in a mouse model13. It is also expressed in blood and hematopoietic cells, which, as mentioned, are sensitive to radiation, explaining the fall in circulating levels of this miRNA after 15 Gy whole thorax irradiation. MiR-21-5p is a well-known lung injury marker30,31,32. But studies with changes in miR-21-5p after radiation have been limited to lung cancer cells23,33 and not in irradiated lung tissue. However some non-radiation studies have associated changes in this microRNA. Circulating miR-21 was suggested by Loboda et al. to be a potential early biomarker of renal fibrosis, because it was shown that upregulation of miR-21 alters metabolic pathways and leads to renal fibrosis34. MiR-21 was also suggested to plays a dynamic role in inflammatory responses34. Since inflammation and fibrosis are considered as major radiation responses in many organs including lungs, it is possible that miR-21 also plays some roles in radiation response in normal tissues. Beside circulation miR-21, exosomal miR-21 was also suggested to have a strong potential to be used as a universal biomarker to identify cancers35. In fact, based on one report, exosomal miR-21 seemed to be better than circulating miR-21 to serve as such a biomarker35. We confirmed a decrease in 150-5p and increase in 21-5p after radiation by RT-qPCR in whole blood from our rat model of thorax radiation. Though these miRNA trended in the same direction with RNA-seq data, there was considerable variation between samples in the libraries, so that the results were not statistically significant. As already mentioned, since the libraries were amplified and pooled these variations could be artifacts. We focused on the changes of miRNA at 2 weeks, since not only as mentioned, that was the time when the most increases in miRNA were detected, but also when most of the mature miRNA were found to be changed after radiation. In addition, we have also established a SPECT/CT biomarker to predict radiation lung injuries36. By this method, the most significant lung injuries, namely the pulmonary cell death measured by 99mTc-labeled Duramycin and pulmonary vascular resistance and vascular permeability measured in isolated perfused lungs were found to be at 2 weeks after 15 Gy in the same rat model.

MiR-144 has been reported to affect sensitivity in radiotherapy by promoting proliferation, migration and invasion of breast cancer cells37. Dysregulation of miR-144 has been described in studies of multiple cancers38,39,40,41. It was suggested to be a tumor suppressor by Matsushita et al.38 and inhibited proliferation but promoted apoptosis and autophagy through targeting the p53-induced glycolysis and apoptosis regulator TIGAR40. Besides the current study, elevation of miR-144 was also reported in many non-cancer studies42,43. MiR-144 is overexpressed in peripheral blood mononuclear cells from patients with pulmonary tuberculosis (TB) and regulated anti-TB immune response in T cells42. Hassan et al. found increase in miR-144 in human bronchial epithelial (HBE) cells exposed to cigarette smoke extract and cadmium. Su et al. showed that miR-144 regulates hematopoiesis and vascular development by repressing expression of meis1 in zebrafish44. In summary miR-144 plays a role in inflammation, immune response and suppression of cell growth, processes that are activated by radiation. It is surprising that we observed increase in this miRNA in whole blood, since circulating cells are decreased by radiation and this miRNA was not increased in irradiated lungs in our model.

MiR-19a-3p has been reported to inhibit breast cancer progression and metastasis by inducing macrophage polarization45. It was also suggested to be involved in inflammatory processes by directly regulating 5-LO (5-lipoxygenase) expression in T lymphocytes46. Busch et al. found that the inhibition of miR-19a-3p with an antagomir led to an increase in 5-LO mRNA expression in T lymphocytes46. We speculate that this miRNA may be induced in response to inflammatory signaling after radiation to down-regulate lipoxygenase metabolites in blood cells such as macrophages and T-lymphocytes.

MiR-142-5p has also been demonstrated to be involved in inflammation21,22,47. Su et al. showed miR-142-5p regulates human and mouse macrophage profibrogenic gene expression in chronic inflammation and models of liver and lung fibrosis47. Increases of miR-142-5p were found in lungs of patients with idiopathic pulmonary fibrosis47. MiR-142-5p was reported to control T-cell responses in vitro and in murine models of graft versus host disease22. Finally miR-142-5p had tumor-suppressive effect in lung cancer cells48,49. In summary, similar to miR-144, miR-142-5p regulates cell growth and inflammation. Interestingly miR-142-5p expression in macrophages also influences fibrosis in the lung47, a phenotype that is induced by radiation. It is possible that detection of increase in this miRNA in spite of a decrease in circulating cells in the blood at 2 weeks after radiation suggests regulation of genes that could lead to the later effects of radiation. Perhaps these immune cells may be responsible for radiation pulmonary injury after infiltration into the lungs.

We also conducted pathway analysis to determine signaling changes in the lung after radiation, based on the changes in miRNA as determined by miRNA-seq. The pathways with the highest scores by Ingenuity Pathway Analysis were “cancer, organismal injury” and “abnormalities and reproductive system disease”, while inflammatory responses received a lower score (results not shown). Pathways derived from changes that we confirmed in the blood of irradiated rats are shown in Fig. 4. To our knowledge this is the first time that miR-144-3p, 144 -5p, and 19a-3p are upregulated in rat blood 2 week after irradiation of the thorax. Their function in inflammatory responses is suggested by the Ingenuity Pathway Analysis (Fig. 4). These miRNA were not changed in the irradiated lung, strongly suggesting they may be upregulated in other cells or tissues. We cannot rule out their presence in exosomes from endothelial cells within the lung. They could also be present in exosomes from other irradiated organs, e.g. the heart, partial bone marrow and blood within the thorax, that were in the field of radiation. We know immune cells are involved in radiation-pneumonitis with inflammatory infiltrates detected in the lung by histology and other diagnostic methods50,51. Vascular damage and remodeling are also found during radiation pneumonitis in human and animal lungs51,52.

Decreases in numbers of circulating blood cells are anticipated and were measured after whole thorax irradiation due to the volume of bone marrow in the field of exposure. WBC have shorter half-lives than RBC so their numbers fall before the RBC. The numbers of WBC recover by 4 weeks. We have observed increases in circulating miRNA we report after radiation, which could also be due to increase in specific transcription induced by radiation in blood cells, but not from changes in numbers of circulating cells, since these numbers were lower at the time points we examined. Future investigation with models involving radiation to the lower hemi-body or heart alone will help to highlight the effect of radiation to the lung on these miRNA changes.

Methods

Animal care and irradiation

All animal protocols and euthanasia criteria were approved by the Institutional Animal Care and Use Committee (IACUC). All methods were performed in accordance to the IACUC guidelines and regulations. Radiation was performed as described previously53. In brief, un-anesthetized 9- to 10-week-old female WAG/RijCmcr rats weighing approximately 140 g were immobilized in a plastic jig and irradiated with 320-kVp orthovoltage system X-rays, with a half-value layer (HVL) of 1.4 mm Cu. Rats were treated with a single dose of 15 Gy to the whole thorax at dose rate of 1.43 Gy/min. The radiation dose was delivered by two equally-weighted lateral beams to improve uniformity. The whole lung, heart and a small amount of liver were in the field. One group of age-matched rats was not irradiated (non-irradiated controls or 0 Gy group) but maintained under identical conditions. Experiments were terminated at 1, 2, 3 or 4 weeks. Age-matched controls were included at the 1 and 4 week time-points.

RNA isolation

Lung tissue from the right superior lobe was cut and ~100 mg was weighed and immersed in TRIzol Reagent (Ambion/RNA by Life Technologies) immediately after sacrifice. Whole blood (0.5 ml) collected by cardiac puncture in an EDTA coated needle and syringe was directly used to extract RNA immediately without any further processing. TRIzol Reagent (Ambion/RNA (Life Technologies)) was used. Total RNA from lung and whole blood were isolated following the protocol provided by the manufacturer. RNA was stored at −80°C for future use.

Small RNA library preparation and sequencing

Total extracted RNA was quantified using Nanodrop 2000 (Thermo Scientific). A260/280 was used to ensure the RNA quality. Equal amounts of RNA from three rats were pooled. The range of A260/280 was maintained between 1.6 and 2.1. Samples with ratios below 1.6 were discarded. Small RNA libraries (36 totals) were generated with the TruSeq Small RNA Library Prep kit (Illumina) following the manufacture’s instruction with minor changes (Table 1). In brief, 1 μg of the pooled total RNA was ligated with 3′ and 5′ adapters. Reverse transcription followed by PCR (based on the 3′ and 5′ adapter sequences for 16 cycles) was used to create cDNA. The amplified cDNA was purified using 6% PAGE Gel and bands between 147nt and 157nt which contains RNA fragment of 22nt and 30nt corresponding to miRNA were cut out using size markers and concentrated by ethanol precipitation. Libraries were visualized and quantitated with Agilent Technologies 2100 Bioanalyzer using DNA-1000 Chip. Next generation sequencing was performed at the Human and Molecular Genetics Center Sequencing Core at MCW15. In brief, prepared libraries were loaded onto the flow cell using a cBot instrument (Illumina) and clusters were generated in the flow cell after a 4-hour-PCR amplification. The flow cell was loaded onto the HiSeq 2000 sequencer. The obtained reads were fed into the in-house CASAVA informatics pipeline, where they were de-multiplexed and aligned to a reference genome miRbase v19.

Analysis of small RNA deep sequencing data

Before the analysis, the adapter sequences were first removed from the output sequence reads by the tool “trim galore”54. Sequences with low quality (base quality < 13) at both ends of reads were further trimmed by the FastQC55 and mapped against miRBase v19 to identify known miRNAs using Bowtie56, including rat miRNAs and homologs of miRNAs known in species other than the rat. Sequence reads that did not map to miRBase were then mapped against mRNA database, Rfam (for other noncoding RNA), and RepBase (for repetitive elements) to remove reads corresponding to transcribed sequences that were not miRNAs. The remaining reads were used to predict new miRNAs with miRanalyzer57. miRanalyzer employs a machine learning approach based on the random forest method. With the default parameter setting, miRanalyzer can obtain the area under the curve value of 97.9% with a true positive rate of 0.79 and a false positive rate of 0.007 for predicting new mammalian miRNAs. To normalize and test differential expression, we used number of reads of known and newly identified miRNAs as input for the Bioconductor DESeq package58. DESeq uses a negative binomial distribution to model reads of miRNAs and to test for differential expression in deep sequencing datasets. The Benjamini-Hochberg method was used to control false discovery rate (FDR) in all statistical tests59.

Real-time quantitative RT-qPCR

Validation of changes in miRNA expression of selected markers was performed by RT-qPCR from the blood of rats used for miRNA-seq as well as from independently irradiated and control rats. Total RNA from each rat were required to meet the following quality criteria: 1) A260/280 > 1.6, 2) yield of RNA was enough to carry out all the RT-qPCR reactions needed for all the primers tested. There was no pre-amplification step prior to PCR. LNA-primers were obtained from Exiqon and reactions were carried out after reverse transcription using miRCURY LNATM Universal RT microRNA PCR kit from Exiqon with sybr green mastermix from Biotool. The relative expression of each miRNA to a reference miR-191-5p was calculated by the formula: 2−∆CT (∆CT=CT (target-reference). Data was then normalized to the mean relative expression of controls, to determine the fold change after radiation. MiR-191-5p was used as reference for normalization because it shows consistent read counts in the libraries by sequencing.

Blood counts

Blood was collected by cardiac puncture into EDTA tubes. White blood cell and red blood cell counts were performed by the Marshfield Laboratories the same day after checking for adequate volume and absence of clots (Marshfield, WI).

Statistical analysis

Sigmaplot software was used to perform statistical analysis on the RT-qPCR results and the blood cell count results. For RT-qPCR, t test was used on 19a-3p and 21-5p; Mann-Whitney U test was used on 142-5p, 144-3p, 144-5p and 150-5p, when the data failed either normality test or equal variance test. For blood cell count, Kruskal-Wallis One Way Analysis of Variance on Ranks Multiple Comparisons versus Control Group (0 Gy) was conducted. Dunn’s method was used as post hoc test.

Additional Information

How to cite this article: Gao, F. et al. Changes in miRNA in the lung and whole blood after whole thorax irradiation in rats. Sci. Rep. 7, 44132; doi: 10.1038/srep44132 (2017).

Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Material

Supplementary Table 1
srep44132-s1.xls (304KB, xls)

Acknowledgments

This study was supported by NIH/NIAID 101898 & 107305 (M.M.) and NIH/NHLBI 116530 & VA Merit Review Award BX001681 (E.J.).

Footnotes

The authors declare no competing financial interests.

Author Contributions F.G. and J.N. performed the library preparation for sequencing. P.L. analyzed the sequencing data. M.Y. and F.G. performed the RT-qPCR. Y.L. helped with library preparation. F.G. performed the pathway analysis. B.F. assisted with irradiation and breeding animals. M.M. designed and guided the study. M.L. and E.J. helped with study design. F.G. and M.M. analyzed the data and wrote the manuscript. P.L., J.N., M.Y., B.F., Y.L. M.L. and E.J. reviewed the manuscript.

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