Gene Expression Profile Analysis for Alternate Wetting and Drying Induced Formation of Iron Plaque on Root Surface of Rice Seedlings

doi:10.16819/j.1001-7216.2017.6115

Abstract

Abstract:

【Objective】Alternate wetting and drying (AWD) could enhance root activity and resistance to environmental stresses, which is the most important management in rice cultivation. AWD could induce the formation of iron plaque on root surface of rice seedlings significantly. However, differentially expressed genes (DEG) of rice roots remains unknown in the process of AWD induced formation of iron plaque. 【Method】In this study, DEG of rice roots were investigated in response to waterlogging (CK), AWD, CK+Fe, AWD+Fe treatments in sand-culture experiments. 【Results】Compared with CK, AWD induced up-regulated expression of 506 DEG and down-regulated expression of 687 DEG. Compared with CK+Fe, AWD+Fe induced up-regulated expression of 308 DEG and down-regulated expression of 179 DEG. Compared with CK, CK+Fe induced up-regulated expression of 728 DEG and down-regulated expression of 1175 DEG. Compared with AWD, AWD+Fe induced up-regulated expression of 1252 DEG and down-regulated expression of 1189 DEG. Results from Venn diagram analysis indicated that 3822 DEG were involved in the process of AWD-induced formation of iron plaque on root surface. Gene Ontology(GO) analysis showed that a total of 270 DEG participated in the oxidation-reduction process, and 165 DEG were related to the functions of redox enzymes. Kyoto encyclopedia of genes and genomes (KEGG) results indicated that DEG involved in organelle class, signal stimulus, photosynthesis, metabolism and synthesis etc. participated the regulatory process of AWD induced formation of iron plaque on root surface. Thirty eight shared genes showed differential expression in process of both AWD and the formation of iron plaque. The proteins encoded by these genes were responsible for the resistance to disease, drought, redox, translocation and metabolism of rice. In comparison to CK, AWD induced 102 DEG in the process of oxidation and reduction, which accounted for 8.25% of the total. AWD enhanced up-regulated expression of genes that were associated with peroxidase, fatty acid oxidase, glycolic oxidase etc. In comparison to CK, AWD raised root activity and amount of iron plaque on root surface by 22.9% and 45.7%, respectively. Results of real-time RT-PCR confirmed the transcriptional group analysis with the correlation coefficient 0.90. 【Conclusion】 AWD induced expression of many differential genes, enhanced root oxidative capacity, and formed iron plaque on root surface. Peroxidase, fatty acid oxidase and glycolic oxidase might be important genes involved in AWD-induced formation of iron plaque on root surface of rice seedlings.

Key words: rice seedling, alternate wetting and drying (AWD), iron plaque, differentially expressed gene (DEG), oxidation-reduction related genes.

摘要：

【目的】干湿交替（AWD）是水稻种植过程中最重要的管理方式,能够提高水稻根系活力、增强抗逆性。研究发现,AWD能明显促进水稻根表铁膜的形成。然而,AWD诱导水稻根表铁膜形成的基因表达谱尚未见报道。【方法】采用砂培试验,研究了长期淹水（CK）、干湿交替（AWD）、长期淹水加Fe²⁺（CK+Fe）和干湿交替加Fe²⁺（AWD+Fe）四个处理下水稻根系基因的差异表达谱。【结果】 AWD处理与CK处理相比,水稻根系有506个差异表达基因（DEG）上调表达,有687个DEG下调表达;AWD+Fe处理与CK+Fe处理相比,有308个DEG上调表达和179个DEG下调表达;CK+Fe处理与CK处理相比,有728个DEG上调表达和1175个DEG下调表达;AWD+Fe处理与AWD处理相比,有1252个DEG上调表达和1189个DEG下调表达。维恩图分析发现,共计有3822个DEG参与了AWD诱导水稻根表铁膜形成过程。基因功能（GO）分析表明,在生物过程中共有270个DEG参与了氧化还原反应过程,分子功能中共有165个DEG与氧化还原酶功能有关。生物通路富集分析（KEGG）结果表明,细胞器类、信号刺激类、光合作用类、生物合成类和代谢类等生物通路参与了AWD诱导水稻根表铁膜的形成过程。AWD和根表铁膜形成过程发现有38个共享DEG,这些基因的蛋白注释与水稻抗病性、抗旱性、细胞壁和细胞膜、氧化还原、蛋白激酶和转运、新陈代谢过程有关。与CK处理相比,AWD处理诱导了氧化还原反应过程相关的基因达102个,占总DEG数的8.25%。AWD处理促进了水稻根系过氧化物酶、脂肪酸氧化酶和乙醇酸氧化酶等相关基因的上调表达。与CK处理相比,AWD处理提高了水稻根系活力和根表铁膜数量,分别为22.9%和45.7%。实时荧光定量PCR验证结果与转录组分析结果基本一致,其相关系数达到0.90。【结论】综上所述,AWD明显诱导了氧化还原反应过程中的相关基因大量表达,增加了根系氧化力,促进根表铁膜的形成;过氧化物酶、脂肪酸氧化酶和乙醇酸氧化酶等相关的基因可能是AWD诱导水稻根表铁膜形成的重要基因。

关键词: 水稻, 干湿交替, 铁膜, 差异表达基因, 氧化还原相关基因

Youqiang FU, Xiaoli YU, Xujian YANG, Hong SHEN. Gene Expression Profile Analysis for Alternate Wetting and Drying Induced Formation of Iron Plaque on Root Surface of Rice Seedlings[J]. Chinese Journal OF Rice Science, 2017, 31(2): 133-148.

傅友强, 于晓莉, 杨旭健, 沈宏. 干湿交替诱导水稻根表铁膜形成的基因表达谱分析[J]. 中国水稻科学, 2017, 31(2): 133-148.

Figures/Tables 15

References 47

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基因 Gene		T_m	GC/%	产物长度 Product length/bp
BGIOSGA001629-TA	F: 5’-CGCCGTCTTCTTCTGCCAGAT-3’ R: 5’-GGTCACTTGCTTGTCCGTGCTT-3’	59.3	55.8	157
BGIOSGA026866-TA	F: 5’-TTCTACCTGGTGGCGCTGCT-3’ R: 5’-TCGACGAAGCGGCTCCACTT-3’	59.7	60.0	200
BGIOSGA029195-TA	F: 5’-GAGGTACGGCACCAGCTTCAAC-3’ R: 5’- CCGCCTCCATCACCTCCATGAA-3’	60.1	59.1	188
BGIOSGA014041-TA	F: 5’-GCCTCCTCCGCATCTTCTTCCA-3’ R: 5’-CGTGGTTGCAGAGTCAGGTTGG-3’	60.2	59.1	115
BGIOSGA014859-TA	F: 5’-TACTACACGGCGGAGCAGGAGA-3’ R: 5’-TTGCAGCTCTTCTTGGCGGACT-3’	60.8	56.8	173
BGIOSGA030543-TA	F: 5’-GGGCTCATCGGGTTCATCTGGA-3’ R: 5’-GCAGGCAATGTGGTGGGTGTT-3’	59.9	58.3	177
BGIOSGA033475-TA	F: 5’-GAGTGGCAGCAGCAGCATTAGT-3’ R: 5’-ACCTGTGTACGGATCGCCTCTC-3’	59.7	56.8	107
BGIOSGA009910-TA	F: 5’-GCTTGGTCGCCGTGTTGGAA-3’ R: 5’-GCATCGCCTCTTTCTCGCTGAA-3’	59.5	57.3	117
BGIOSGA017964-TA	F: 5’-TGGAGCAAGGCGGAGGACAA-3’ R: 5’-CCACGAGCACCTGGTAGTGTTC-3’	59.3	59.5	142
OsActin	F: 5’-CCGCCAGAGAGGAAGTACAG-3’ R: 5’-AAGCACTTCCTGTGGACGAT-3’	59.4	55.1	131

基因 Gene		T_m	GC/%	产物长度 Product length/bp
BGIOSGA001629-TA	F: 5’-CGCCGTCTTCTTCTGCCAGAT-3’ R: 5’-GGTCACTTGCTTGTCCGTGCTT-3’	59.3	55.8	157
BGIOSGA026866-TA	F: 5’-TTCTACCTGGTGGCGCTGCT-3’ R: 5’-TCGACGAAGCGGCTCCACTT-3’	59.7	60.0	200
BGIOSGA029195-TA	F: 5’-GAGGTACGGCACCAGCTTCAAC-3’ R: 5’- CCGCCTCCATCACCTCCATGAA-3’	60.1	59.1	188
BGIOSGA014041-TA	F: 5’-GCCTCCTCCGCATCTTCTTCCA-3’ R: 5’-CGTGGTTGCAGAGTCAGGTTGG-3’	60.2	59.1	115
BGIOSGA014859-TA	F: 5’-TACTACACGGCGGAGCAGGAGA-3’ R: 5’-TTGCAGCTCTTCTTGGCGGACT-3’	60.8	56.8	173
BGIOSGA030543-TA	F: 5’-GGGCTCATCGGGTTCATCTGGA-3’ R: 5’-GCAGGCAATGTGGTGGGTGTT-3’	59.9	58.3	177
BGIOSGA033475-TA	F: 5’-GAGTGGCAGCAGCAGCATTAGT-3’ R: 5’-ACCTGTGTACGGATCGCCTCTC-3’	59.7	56.8	107
BGIOSGA009910-TA	F: 5’-GCTTGGTCGCCGTGTTGGAA-3’ R: 5’-GCATCGCCTCTTTCTCGCTGAA-3’	59.5	57.3	117
BGIOSGA017964-TA	F: 5’-TGGAGCAAGGCGGAGGACAA-3’ R: 5’-CCACGAGCACCTGGTAGTGTTC-3’	59.3	59.5	142
OsActin	F: 5’-CCGCCAGAGAGGAAGTACAG-3’ R: 5’-AAGCACTTCCTGTGGACGAT-3’	59.4	55.1	131

处理 Treatment	总原始读序数Total raw reads	接头污染 Adaptor pollution	多N序列 More N sequences	低质序列 Low sequences	干净序列 Clear sequences	Clean-Q₂₀/%
CK-1	15 203 921 (100%)	7 879 (0.05%)	8 096 (0.05%)	226 110 (1.49%)	14 961 836 (98.41%)	99.03
CK-2	14 617 585 (100%)	3 596 (0.02%)	7 679 (0.05%)	218 591 (1.50%)	14 387 719 (98.43%)	99.02
CK-3	16 294 296 (100%)	4 603 (0.03%)	8 909 (0.05%)	239 703 (1.47%)	16 041 081 (98.45%)	99.03
AWD-1	15 761 265 (100%)	7 966 (0.05%)	8 422 (0.05%)	243 872 (1.55%)	15 501 005 (98.35%)	98.99
AWD-2	17 182 802 (100%)	6 061 (0.04%)	9 178 (0.05%)	261 475 (1.52%)	16 906 088 (98.39%)	99.00
AWD-3	15 568 129 (100%)	9 138 (0.06%)	8 057 (0.05%)	235 384 (1.51%)	15 315 550 (98.38%)	99.02
CK+Fe-1	14 026 907 (100%)	7 217 (0.05%)	7 497 (0.05%)	210 847 (1.50%)	13 801 346 (98.39%)	99.01
CK+Fe-2	15 543 510 (100%)	13 312 (0.09%)	8 186 (0.05%)	225 777 (1.45%)	15 296 235 (98.41%)	99.04
CK+Fe-3	17 756 771 (100%)	45 874 (0.26%)	576 (0.00%)	271 929 (1.53%)	17 438 392 (98.21%)	98.77
AWD+Fe-1	16 562 694 (100%)	25 236 (0.15%)	487 (0.00%)	276 876 (1.67%)	16 260 095 (98.17%)	98.65
AWD+Fe-2	17 369 964 (100%)	39 004 (0.22%)	544 (0.00%)	265 196 (1.53%)	17 065 220 (98.25%)	98.78
AWD+Fe-3	13 292 393 (100%)	11 353 (0.09%)	1 501 (0.01%)	197 158 (1.48%)	13 082 381 (98.42%)	98.97

处理 Treatment	总原始读序数Total raw reads	接头污染 Adaptor pollution	多N序列 More N sequences	低质序列 Low sequences	干净序列 Clear sequences	Clean-Q₂₀/%
CK-1	15 203 921 (100%)	7 879 (0.05%)	8 096 (0.05%)	226 110 (1.49%)	14 961 836 (98.41%)	99.03
CK-2	14 617 585 (100%)	3 596 (0.02%)	7 679 (0.05%)	218 591 (1.50%)	14 387 719 (98.43%)	99.02
CK-3	16 294 296 (100%)	4 603 (0.03%)	8 909 (0.05%)	239 703 (1.47%)	16 041 081 (98.45%)	99.03
AWD-1	15 761 265 (100%)	7 966 (0.05%)	8 422 (0.05%)	243 872 (1.55%)	15 501 005 (98.35%)	98.99
AWD-2	17 182 802 (100%)	6 061 (0.04%)	9 178 (0.05%)	261 475 (1.52%)	16 906 088 (98.39%)	99.00
AWD-3	15 568 129 (100%)	9 138 (0.06%)	8 057 (0.05%)	235 384 (1.51%)	15 315 550 (98.38%)	99.02
CK+Fe-1	14 026 907 (100%)	7 217 (0.05%)	7 497 (0.05%)	210 847 (1.50%)	13 801 346 (98.39%)	99.01
CK+Fe-2	15 543 510 (100%)	13 312 (0.09%)	8 186 (0.05%)	225 777 (1.45%)	15 296 235 (98.41%)	99.04
CK+Fe-3	17 756 771 (100%)	45 874 (0.26%)	576 (0.00%)	271 929 (1.53%)	17 438 392 (98.21%)	98.77
AWD+Fe-1	16 562 694 (100%)	25 236 (0.15%)	487 (0.00%)	276 876 (1.67%)	16 260 095 (98.17%)	98.65
AWD+Fe-2	17 369 964 (100%)	39 004 (0.22%)	544 (0.00%)	265 196 (1.53%)	17 065 220 (98.25%)	98.78
AWD+Fe-3	13 292 393 (100%)	11 353 (0.09%)	1 501 (0.01%)	197 158 (1.48%)	13 082 381 (98.42%)	98.97

处理 Treatment	有效Reads数Effective reads	总比对数 Total mapped	多序列比对Multiple mapped	唯一比对Uniquely mapped	正链比对 Reads map to '+'	负链比对Reads map to '-'
CK-1	14 961 836 (100%)	14 064 974 (94.01%)	1 399 518 (9.35%)	12 665 456 (84.65%)	6 682 511 (44.66%)	7 382 463 (49.34%)
CK-2	14 387 719 (100%)	13 418 012 (93.26%)	1 304 324 (9.07%)	12 113 688 (84.19%)	6 395 522 (44.45%)	7 022 490 (48.81%)
CK-3	16 041 081 (100%)	15 096 703 (94.11%)	1 252 800 (7.81%)	13 843 903 (86.30%)	6 853 649 (44.75%)	7 814 056 (48.71%)
AWD-1	15 501 005 (100%)	14 458 761 (93.28%)	1 408 866 (9.09%)	13 049 895 (84.19%)	6 921 799 (44.65%)	7 536 962 (48.62%)
AWD-2	16 906 088 (100%)	15 898 780 (94.04%)	2 003 341 (11.85%)	13 895 439 (82.19%)	7 409 954 (43.83%)	8 488 826 (50.21%)
AWD-3	15 315 550 (100%)	14 292 666 (93.32%)	1 286 870 (8.40%)	13 005 796 (84.92%)	6 853 649 (44.75%)	7 439 017 (48.57%)
CK+Fe-1	13 801 346 (100%)	12 827 342 (92.94%)	1 270 100 (9.20%)	11 557 242 (83.74%)	6 111 387 (44.28%)	6 715 955 (48.66%)
CK+Fe-2	15 296 235 (100%)	14 278 682 (93.35%)	1 565 420 (10.23%)	12 713 262 (83.11%)	6 757 606 (44.18%)	7 521 076 (49.17%)
CK+Fe-3	17 438 392 (100%)	16 001 385 (91.76%)	1 368 718 (7.85%)	14 632 667 (83.91%)	7 756 673 (44.48%)	8 244 712 (47.28%)
AWD+Fe-1	16 260 095 (100%)	15 235 534 (93.70%)	1 559 671 (9.59%)	13 675 863 (84.11%)	7 277 032 (44.75%)	7 958 502 (48.94%)
AWD+Fe-2	17 065 220 (100%)	15 859 868 (92.94%)	1 430 141 (8.38%)	14 429 727 (84.56%)	7 619 349 (44.65%)	8 240 519 (48.29%)
AWD+Fe-3	13 082 381 (100%)	12 121 909 (92.66%)	1 093 176 (8.36%)	11 028 733 (84.30%)	5 830 048 (44.56%)	6 291 861 (48.09%)