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Acta Agronomica Sinica ›› 2021, Vol. 47 ›› Issue (5): 837-846.doi: 10.3724/SP.J.1006.2021.04173

• CROP GENETICS & BREEDING · GERMPLASM RESOURCES · MOLECULAR GENETICS • Previous Articles     Next Articles

Integrated analysis between folate metabolites profiles and transcriptome of panicle in foxtail millet

MA Gui-Fang1(), MAN Xia-Xia1, ZHANG Yi-Juan2, GAO Hao1, SUN Zhao-Xia1,3, LI Hong-Ying1,3, HAN Yuan-Huai1,3, HOU Si-Yu1,3,*()   

  1. 1College of Agriculture / Institute of Agricultural Bioengineer, Shanxi Agricultural University, Taigu 030801, Shanxi, China
    2College of Life Sciences, Shanxi Agricultural University, Taigu 030801, Shanxi, China
    3Shanxi Key Laboratory of Genetic Resources and Breeding in Minor Crops, Taiyuan 030031, Shanxi, China
  • Received:2020-07-29 Accepted:2020-11-13 Online:2021-05-12 Published:2020-12-15
  • Contact: HOU Si-Yu E-mail:1783526621@qq.com;bragren123@163.com
  • Supported by:
    National Key Research and Development Program of China(2018YFD1000705-2);Cultivation Project of Excellent Scientific Research Achievements in Shanxi Universities(2019KJ020);Shanxi Agricultural Valley Construction Research Project(SXNGJSKYZX201702);Shanxi Agricultural University Youth Science and Technology Innovation Found Project(2019005)

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Abstract:

Folate (FA) is an important donor for energy metabolism, amino acid and nucleic acid synthesis, and participates in the intracellular carbon unit transfer reaction. In previous study, we found that folate content in panicle of foxtail millet was higher than other cereal crops, but the composition characteristics of folate metabolites are still unclear. In this study, in order to explore the expression patterns of folic acid components and folic acid metabolism pathway genes and their correlation with variable shear, and to predict the protein interaction network of folic acid synthesis pathway genes, folate metabolome was performed on three panicle development stages using the middle part of the ‘Jingu 21’ panicles as the experimental materials by RNA-seq. The results showed that the total folate content decreased with panicle development stage, and the contents of 5-methyl tetrahydrofolate (5-M-THF) and 10-formyl folate (10-F-FA) were the main components of panicle development. The expression pattern analysis of 17 key genes of folate synthesis can be divided into two groups during the panicle development in foxtail millet. The alternative splicing showed that the 16 key genes for folate synthesis produced transcription start site (TSS) and transcription terminal site (TTS) during the panicle development, the number of other types of alternative splicing was different at each stage, and this specific alternative splicing affects folate content. In addition, methylation pathway, hormone signaling pathway and immune pathway related genes showed a certain correlation with different folate metabolite content, and we preliminarily hypothesized that the expression of folate synthesis related and coupling pathway genes would influence the folate content during the panicle development. The different expression of ADCS, DHFR2, and GGH may be the main reason for the influence of folate content in panicle, and could be used as key target gen 837-846es for folate biofortification of foxtail millet by genetic engineering technology in the future.

Key words: foxtail millet, folate, transcriptome, metabolomics

Fig. 1

Biosynthesis of folate in plant cells"

Table S1

MS/MS parameters of 12 folate species"

化合物
Analytes		 保留时间
tr (min)		 前体
Precursorion (m/z)		 产物
Production (m/z)		 碰撞能量
Collision energy (eV)
5-甲基四氢叶酸5-M-THF 3.754 460 313 20
双谷氨酰基5-甲基四氢叶酸酯5-M-THFGlu2 4.097 589 313 20
三谷氨酰基5-甲基四氢叶酸酯5-M-THFGlu3 4.867 718 313 30
四谷氨酰基5-甲基四氢叶酸酯5-M-THFGlu4 5.607 847 313 40
5-甲酰四氢叶酸5-F-THF 7.072 475 328 30
双谷氨酰胺基5-甲酰基四氢叶酸5-F-THFGlu2 7.338 603 327 20
三谷氨酰胺基5-甲酰基四氢叶酸5-F-THFGlu3 7.485 732 327 30
四谷氨酰胺基5-甲酰基四氢叶酸5-F-THFGlu4 7.767 861 327 40
四氢叶酸THF 2.668 446 299 30
10-甲酰叶酸10-F-FA 6.832 470 295 20
5,10-亚甲基四氢叶酸5,10-CH=THF 5.830 456 412 30
叶酸FA 8.131 442 295 20

Fig. 2

Folate content and compounds at different panicle development stages S1: the middle and early grain filling stage; S2: the middle and late grain filling stage; S3: harvesting stage. Different lowercase letters above the bars mean significantly different at the 0.05 probability level."

Fig. S1

Relative heat map of expression quantity of pairwise S1: the middle and early grain filling stage; S2: the middle and late grain filling stage; S3: harvesting stage."

Table S2

Distribution of sample reads"

样品
Sample		 原始序列
Raw reads		 过滤后序列
Clean reads		 高质量序列
Clean reads (%)		 比对到基因组序列
Mapped reads		 锚定比例
Mapped (%)		 单一位置序列
Unique mapped reads (%)		 过滤后Q30比例
Q30 after filter (%)
S1-1 47381498 44433844 93.77 40683987 91.56 97.23 93.50
S1-2 49046244 45860746 93.50 41996800 91.57 97.26 93.27
S1-3 46450810 43418190 93.47 39219806 90.33 97.14 94.01
S2-1 43629752 41410962 94.91 39262950 94.81 92.57 93.47
S2-2 51156392 48385928 94.58 45812419 94.68 92.90 93.69
S2-3 47754204 45141994 94.52 42655979 94.49 93.15 93.61
S3-1 45693728 43128684 94.38 36319571 84.21 96.07 93.63
S3-2 43824250 41043096 93.65 34490126 84.03 96.08 93.48
S3-3 46573564 43758700 93.95 36830384 84.17 95.96 93.51

Fig. 3

Number of DEGs expression (a) and Venn diagram of differentially expressed genes (b) at different panicle development stages Abbreviations are the same as those given in Fig. 2."

Fig. S2

Functional annotation of genes in different databases S1: the middle and early grain filling stage; S2: the middle and late grain filling stage; S3: harvesting stage."

Fig. S3

KEGG pathway and classification S1: the middle and early grain filling stage; S2: the middle and late grain filling stage; S3: harvesting stage.e 0.05 and 0.01 probability levels, respectively."

Fig. 4

Number of alternative splicing events at different panicle development stages Abbreviations are the same as those given in Fig. 2."

Table S3

Alternative splicing events of 16 key genes in folate synthesis"

基因名称
Gene ID		 S1 S2 S3
染色体
Chr.		 起始位点
Event_start		 终止位点
Event_end		 剪切类型
Event_type		 染色体
Chr.		 起始位点
Event_start		 终止位点
Event_end		 剪切类型
Event_type		 染色体
Chr.		 起始位点
Event_Start		 终止位点
Event_end		 剪切类型
Event_type
ADCS Si005831m.g scaffold_4 36720083 36720416 TSS scaffold_4 36720083 36720714 TSS scaffold_4 36720083 36720666 TSS
scaffold_4 36714947 36715745 TTS scaffold_4 36714906 36715745 TTS scaffold_4 36719830 36720403 TSS
scaffold_4 36716869 36717210 IR scaffold_4 36718684 36718804 AE scaffold_4 36714937 36715745 TTS
scaffold_4 36718586 36718684 IR scaffold_4 36718697 36718804 AE scaffold_4 36714904 36715745 TTS
scaffold_4 36718586 36718664 IR scaffold_4 36718664 36718804 AE scaffold_4 36718586 36718697 IR
scaffold_4 36718586 36718697 IR scaffold_4 36718586 36718684 IR
scaffold_4 36718684 36718804 AE scaffold_4 36718697 36718804 AE
scaffold_4 36718697 36718804 AE scaffold_4 36718684 36718804 AE
scaffold_4 36718664 36718804 AE scaffold_4 36718664 36718804 AE
scaffold_4 36718335 36718586 AE
scaffold_4 36718335 36718606 AE
ADCL1 Si017411m.g scaffold_1 10445656 10445931 TSS scaffold_1 10445656 10445931 TSS scaffold_1 10445656 10445931 TSS
scaffold_1 10451775 10452094 TTS scaffold_1 10451775 10452094 TTS scaffold_1 10451775 10452094 TTS
scaffold_1 10448493 10450227 ES scaffold_1 10448493 10448548 ES
ADCL3 Si002147m.g scaffold_5 286063 286217 TSS scaffold_5 286063 286217 TSS scaffold_5 286063 286217 TSS
scaffold_5 284297 285428 TTS scaffold_5 284297 285428 TTS scaffold_5 284297 285428 TTS
scaffold_5 284297 285702 TTS scaffold_5 284297 285702 TTS scaffold_5 284297 285702 TTS
GTPCHI
Si021939m.g		 scaffold_3 1626381 1626634 TSS scaffold_3 1626381 1626634 TSS scaffold_3 1626381 1626634 TSS
scaffold_3 1622984 1624611 TTS scaffold_3 1622984 1624611 TTS scaffold_3 1622984 1624611 TTS
DHNA1 Si030989m.g scaffold_2 39956335 39956388 TSS scaffold_2 39956335 39956388 TSS scaffold_2 39956335 39956388 TSS
scaffold_2 39957340 39957698 TTS scaffold_2 39957340 39957698 TTS scaffold_2 39957340 39957698 TTS
DHNA2 Si014629m.g scaffold_6 35419038 35419453 TSS scaffold_6 35419038 35419453 TSS scaffold_6 35419038 35419453 TSS
scaffold_6 35417984 35418412 TTS scaffold_6 35417984 35418412 TTS scaffold_6 35417984 35418412 TTS
HPPK1/DHP
S1
Si000970m.g		 scaffold_5 2400515 2400903 TSS scaffold_5 2400518 2400903 TSS scaffold_5 2400656 2400903 TSS
scaffold_5 2401861 2403377 TTS scaffold_5 2401861 2403377 TTS scaffold_5 2401861 2403377 TTS
scaffold_5 2401898 2403377 TTS scaffold_5 2401898 2403377 TTS
HPPK2/DHP
S2
Si026336m.g		 scaffold_8 927507 927755 TSS scaffold_8 927507 927751 TSS scaffold_8 927507 927748 TSS
scaffold_8 927568 927755 TSS scaffold_8 927568 927743 TSS scaffold_8 927568 927748 TSS
scaffold_8 925298 926981 TTS scaffold_8 925298 927006 TTS scaffold_8 925298 927006 TTS
scaffold_8 925298 927006 TTS scaffold_8 928346 928456 TSS
DHFS Si029390m.g scaffold_2 45202748 45203091 TSS scaffold_2 45202748 45203091 TSS scaffold_2 45202748 45203091 TSS
scaffold_2 45197629 45198523 TTS scaffold_2 45197629 45198523 TTS scaffold_2 45197629 45198523 TTS
scaffold_2 45200269 45200995 IR scaffold_2 45200269 45200995 IR
scaffold_2 45200269 45200991 IR
DHFR1 Si029497m.g scaffold_2 46935689 46935812 TSS scaffold_2 46935689 46935812 TSS scaffold_2 46935689 46935812 TSS
scaffold_2 46941948 46942341 TTS scaffold_2 46941948 46942330 TTS scaffold_2 46941948 46942333 TTS
scaffold_2 46941454 46941535 IR scaffold_2 46936888 46936960 ES scaffold_2 46936888 46936960 ES
scaffold_2 46941454 46941535 IR scaffold_2 46939375 46939410 ES
scaffold_2 46941454 46941535 IR
DHFR2 Si026222m.g scaffold_8 23086218 23086319 TSS scaffold_8 23086218 23086319 TSS scaffold_8 23086218 23086319 TSS
scaffold_8 23093650 23094000 TTS scaffold_8 23093650 23094000 TTS scaffold_8 23093650 23093993 TTS
scaffold_8 23090412 23090460 ES scaffold_8 23087345 23087422 ES scaffold_8 23087345 23087422 ES
scaffold_8 23087345 23087422 ES scaffold_8 23087345 23087400 ES scaffold_8 23090412 23090460 ES
scaffold_8 23087422 23087911 IR scaffold_8 23087422 23087911 IR scaffold_8 23087422 23087911 IR
scaffold_8 23087345 23088437 AE scaffold_8 23087400 23087911 IR scaffold_8 23087345 23088437 AE
scaffold_8 23087911 23088437 AE scaffold_8 23087345 23087422 AE
scaffold_8 23087911 23088437 AE
scaffold_8 23087345 23088437 AE
FPGS1 Si034828m.g scaffold_9 14396623 14396875 TSS scaffold_9 14396623 14396875 TSS scaffold_9 14396623 14396875 TSS
scaffold_9 14396623 14397092 TSS scaffold_9 14401708 14401931 TTS scaffold_9 14396623 14397092 TSS
scaffold_9 14401708 14401931 TTS scaffold_9 14404546 14404875 TTS scaffold_9 14401708 14401931 TTS
scaffold_9 14404546 14405008 TTS scaffold_9 14398067 14398207 IR scaffold_9 14398067 14398207 IR
scaffold_9 14398067 14398207 IR scaffold_9 14396965 14397092 AE scaffold_9 14396965 14397092 AE
scaffold_9 14399581 14399711 IR scaffold_9 14396965 14397082 AE scaffold_9 14396965 14397082 AE
scaffold_9 14396965 14397082 AE
scaffold_9 14396965 14397092 AE
FPGS2 Si035045m.g scaffold_9 58273141 58273265 TSS scaffold_9 58273141 58273469 TSS scaffold_9 58273141 58273265 TSS
scaffold_9 58273141 58273469 TSS scaffold_9 58273141 58273265 TSS scaffold_9 58273141 58273261 TSS
scaffold_9 58277131 58277474 TTS scaffold_9 58277131 58277474 TTS scaffold_9 58273141 58273469 TSS
scaffold_9 58273333 58273469 AE scaffold_9 58273333 58273469 AE scaffold_9 58277253 58277474 TTS
scaffold_9 58273345 58273469 AE scaffold_9 58273345 58273469 AE scaffold_9 58273333 58273469 AE
scaffold_9 58273345 58273469 AE
GGH Si022538m.g scaffold_3 13171089 13171472 TSS scaffold_3 13171089 13171472 TSS scaffold_3 13171089 13171472 TSS
scaffold_3 13174465 13174749 TTS scaffold_3 13174465 13174749 TTS scaffold_3 13174465 13174749 TTS

Fig. 5

Relative expression of folate metabolism-related genes Abbreviations are the same as those given in Fig. 2."

Table S4

Folate synthesis genes expression levels in different panicle development stages"

基因名称
Gene ID		 S1 S2 S3
ADCS 11.851 5.611 6.334
ADCL1 10.768 9.268 5.769
ADCL2 0.124 0.0873 0.167
ADCL3 28.234 37.516 33.193
GTPCHI 26.686 24.936 48.024
DHNA1 5.884 2.425 4.933
DHNA2 22.406 21.536 84.926
HPPK1 5.676 2.074 5.595
HPPK2 11.536 6.834 10.729
DHFS 5.078 3.889 5.610
DHFR1 36.485 28.580 13.507
DHFR2 18.495 13.686 11.735
FPGS1 8.169 4.998 11.119
FPGS2 35.046 16.451 35.591
GGH 34.266 37.316 22.790
DHPS1 5.676 2.074 5.595
DHPS2 11.536 6.834 10.729

Table S5

Correlation analysis between key genes of folate synthesis and folate compounds"

5-M-THF 5-M-THFGlu3 5-M-THFGlu4 5,10-CH=THF 10-F-FA 5-F-THF 5-F-THFGlu2 5-FTHFGlu4 FA ADCS ADCL1 ADCL2 ADCL3 GCHI DHNA1 DHNA2 HPPK1 HPPK2 DHFS DHFR1 DHFR2 FPGS1 FPGS2 GGH DHPS1 DHPS2
5-M-THF 1
5-M-THFGlu3 -0.454 1
5-M-THFGlu4 -0.705 0.952 1
5,10-CH=THF 0.551 0.493 0.204 1
10-F-FA 0.703 -0.953 -1.000** -0.206 1
5-F-THF 0.998* -0.400 -0.661 0.600 0.659 1
5-F-THFGlu2 0.983 -0.283 -0.562 0.695 0.560 0.992 1
5-F-THFGlu4 0.231 0.762 0.528 0.939 -0.529 0.289 0.406 1
FA 0.334 -0.991 -0.904 -0.602 0.905 0.277 0.155 -0.840 1
ADCS 0.350 -0.994 -0.911 -0.589 0.912 0.293 0.172 -0.831 1.000* 1
ADCL1 0.937 -0.737 -0.908 0.225 0.907 0.914 0.857 -0.124 0.643 0.655 1
ADCL2 -0.915 0.056 0.358 -0.841 -0.356 -0.937 -0.973 -0.604 0.075 -0.716 0.058 1
ADCL3 0.021 0.881 0.695 0.846 -0.696 0.081 0.204 0.978 -0.935 -0.929 -0.330 -0.423 1
GCHI -1.000** 0.447 0.699 -0.558 -0.698 -0.999* -0.984 -0.239 -0.327 -0.342 -0.934 0.918 -0.029 1
DHNA1 -0.309 -0.707 -0.457 -0.964 0.459 -0.366 -0.479 -0.997 0.793 0.783 0.043 0.667 -0.957 0.317 1
DHNA2 -0.999* 0.497 0.738 -0.510 -0.737 -0.994 -0.973 -0.184 -0.379 -0.395 -0.953 0.894 0.028 -0.998* 0.263 1
HPPK1 -0.535 -0.510 -0.223 -1.000* 0.225 -0.584 -0.681 -0.946 0.618 0.605 -0.206 0.830 -0.856 0.541 0.969 0.493 1
HPPK2 -0.410 -0.626 -0.358 -0.987 0.360 -0.464 -0.571 -0.982 0.722 0.711 -0.066 0.743 -0.920 0.417 0.994 0.366 0.990 1
DHFS -0.777 -0.207 0.101 -0.953 -0.099 -0.814 -0.880 -0.792 0.333 0.317 -0.508 0.965 -0.645 0.782 0.839 0.746 0.947 0.893 1
DHFR1 0.919 -0.769 -0.927 0.177 0.927 0.894 0.831 -0.172 0.679 0.691 0.999* -0.681 -0.375 -0.916 0.091 -0.937 -0.158 -0.017 -0.466 1
DHFR2 0.680 -0.962 -0.999* -0.237 0.999* 0.635 0.533 -0.556 0.918 0.925 0.893 -0.326 -0.719 -0.674 0.487 -0.714 0.256 0.390 -0.067 0.914 1
FPGS1 -0.885 -0.013 0.293 -0.876 -0.291 -0.911 -0.956 -0.657 0.143 0.126 -0.667 0.998* -0.484 0.889 0.716 0.862 0.867 0.788 0.981 -0.630 -0.260 1
FPGS2 -0.572 -0.471 -0.179 -1.000* 0.181 -0.620 -0.713 -0.930 0.582 0.568 -0.249 0.854 -0.832 0.578 0.957 0.532 0.999* 0.983 0.961 -0.202 0.213 0.888 1
GGH 0.990 -0.325 -0.598 0.662 0.597 0.997 0.999* 0.365 0.199 0.215 0.879 -0.962 0.160 -0.991 -0.439 -0.982 -0.647 -0.534 -0.858 0.855 0.571 -0.941 -0.681 1
DHPS1 -0.535 -0.510 -0.223 -1.000* 0.225 -0.584 -0.681 -0.946 0.618 0.605 -0.206 0.830 -0.856 0.541 0.969 0.493 1.000** 0.990 0.947 -0.158 0.256 0.867 0.999* -0.647 1
DHPS2 -0.410 -0.626 -0.358 -0.987 0.360 -0.464 -0.571 -0.982 0.722 0.711 -0.066 0.743 -0.920 0.417 0.994 0.366 0.990 1.000** 0.893 -0.017 0.390 0.788 0.983 -0.534 0.990 1

Fig. 6

Relative expression of folate-coupled one-carbon metabolism (a) and hormone signaling pathways (b) Abbreviations are the same as those given in Fig. 2."

Table S6

Correlation analysis of key genes of carbon pathway and folate compounds"

5-M-THF 5-M-THFGlu3 5-M-THFGlu4 5,10-CH=THF 10-F-FA 5-F-THF 5-F-THFGlu2 5-FTHFGlu4 FA MET1 MET2 MET3 CMT1 CMT3 CMT2 DDM2 DDM1 DME1 DME3 DRM1 SHMT3 PGFT FTHFS FTHFD MTRF SHMT1 SHMT2 ATM MTHFS MTHFR
5-M-THF 1
5-M-THFGlu3 -0.454 1
5-M-THFGlu4 -0.705 0.952 1
5,10-CH=THF 0.551 0.493 0.204 1
10-F-FA 0.703 -0.953 -1.000** -0.206 1
5-F-THF 0.998* -0.400 -0.661 0.600 0.659 1
5-F-THFGlu2 0.983 -0.283 -0.562 0.695 0.560 0.992 1
5-F-THFGlu4 0.231 0.762 0.528 0.939 -0.529 0.289 0.406 1
FA 0.334 -0.991 -0.904 -0.602 0.905 0.277 0.155 -0.840 1
MET1 0.735 0.270 -0.037 0.971 0.036 0.775 0.848 0.829 -0.393 1
MET2 0.759 0.235 -0.073 0.962 0.071 0.797 0.866 0.809 -0.360 0.999* 1
MET3 0.684 -0.961 -1.000* -0.232 1.000* 0.639 0.538 -0.552 0.916 0.009 0.045 1
CMT1 -0.220 0.969 0.847 0.693 -0.848 -0.161 -0.036 0.899 -0.993 0.500 0.468 -0.862 1
CMT3 0.637 -0.976 -0.996 -0.292 0.996 0.590 0.484 -0.603 0.939 -0.054 -0.018 0.998* -0.892 1
CMT2 0.860 -0.846 -0.968 0.048 0.968 0.828 0.751 -0.298 0.769 0.286 0.320 0.961 -0.687 0.941 1
DDM2 0.900 -0.021 -0.326 0.859 0.324 0.925 0.965 0.631 -0.109 0.957 0.967 0.299 0.227 0.238 0.552 1
DDM1 0.987 -0.590 -0.808 0.411 0.807 0.976 0.941 0.073 0.480 0.618 0.646 0.791 -0.372 0.752 0.930 0.820 1
DME1 0.933 -0.744 -0.912 0.215 0.911 0.910 0.851 -0.133 0.650 0.443 0.475 0.900 -0.555 0.871 0.986 0.684 0.979 1
DME3 0.239 -0.974 -0.858 -0.678 0.859 0.181 0.057 -0.889 0.995 -0.482 -0.450 0.872 -1.000* 0.901 0.702 -0.207 0.391 0.572 1
DRM1 0.678 0.347 0.044 0.987 -0.046 0.721 0.802 0.872 -0.466 0.997 0.993 -0.072 0.568 -0.134 0.208 0.930 0.553 0.369 -0.551 1
SHMT3 0.494 -0.999* -0.965 -0.453 0.966 0.441 0.326 -0.732 0.985 -0.226 -0.191 0.972 -0.957 0.985 0.869 0.067 0.626 0.773 0.962 -0.304 1
PGFT -0.996 0.375 0.640 -0.622 -0.639 -1.000* -0.995 -0.315 -0.251 -0.792 -0.813 -0.618 0.134 -0.567 -0.812 -0.935 -0.970 -0.899 -0.154 -0.740 -0.416 1
FTHFS -0.111 0.936 0.784 0.768 -0.785 -0.052 0.073 0.941 -0.974 0.591 0.562 -0.801 0.994 -0.837 -0.603 0.332 -0.268 -0.461 -0.992 0.655 -0.919 0.024 1
FTHFD -0.765 -0.226 0.082 -0.959 -0.080 -0.802 -0.871 -0.803 0.351 -0.999* -1.000** -0.054 -0.460 0.009 -0.329 -0.969 -0.653 -0.483 0.442 -0.992 0.182 0.818 -0.555 1
MTRF -0.803 -0.166 0.143 -0.940 -0.141 -0.837 -0.899 -0.765 0.293 -0.994 -0.998* -0.114 -0.405 -0.052 -0.386 -0.982 -0.698 -0.536 0.386 -0.983 0.121 0.852 -0.503 .0998* 1
SHMT1 -0.968 0.216 0.503 -0.743 -0.502 -0.981 -0.998* -0.468 -0.087 -0.882 -0.898 -0.479 -0.033 -0.423 -0.704 -0.981 -0.916 -0.813 0.012 -0.841 -0.260 0.986 -0.142 0.903 0.927 1
SHMT2 -0.115 -0.833 -0.624 -0.892 0.625 -0.175 -0.296 -0.993 0.898 -0.758 -0.734 0.646 -0.944 0.692 0.408 -0.536 0.044 0.249 0.937 -0.808 0.807 0.202 -0.974 0.728 0.685 0.361 1
ATM 0.632 -0.977 -0.995 -0.298 0.995 0.584 0.479 -0.608 0.942 -0.060 -0.024 0.998* -0.895 1.000** 0.939 0.232 0.747 0.868 0.904 -0.141 0.986 -0.562 -0.841 0.015 -0.046 -0.417 0.697 1
MTHFS -0.991 0.571 0.795 -0.433 -0.793 -0.981 -0.949 -0.096 -0.459 -0.637 -0.664 -0.777 0.350 -0.736 -0.921 -0.833 -1.000* -0.974 -0.369 -0.572 -0.608 0.975 0.246 0.670 0.714 0.925 -0.021 -0.732 1
MTHFR 0.583 -0.989 -0.987 -0.357 0.988 0.533 0.423 -0.656 0.961 -0.122 -0.087 0.991 -0.921 0.998* 0.916 0.171 0.705 0.835 0.929 -0.202 0.994 -0.510 -0.873 0.077 0.016 -0.360 0.740 0.998* -0.688 1

Table S7

Correlation analysis between key genes of hormone signaling pathway and folate compounds"

5-M-THF 5-M-THFGlu3 5-M-THFGlu4 5,10-CH=THF 10-F-FA 5-F-THF 5-F-THFGlu2 FA ARF GH3 PP2C ABF MPK6 EIN3 BRI1 BKR1 BIN2 NPP1
5-M-THF 1.000
5-M-THFGlu3 -0.454 1.000
5-M-THFGlu4 -0.705 0.952 1.000
5,10-CH=THF 0.551 0.493 0.204 1.000
10-F-FA 0.703 -0.953 -1.000** -0.206 1.000
5-F-THF 0.998* -0.400 -0.661 0.600 0.659 1.000
5-F-THFGlu2 0.983 -0.283 -0.562 0.695 0.560 0.992 1.000
FA 0.334 -0.992 -0.904 -0.602 0.905 0.277 0.155 1.000
ARF 0.358 -0.994 -0.915 -0.582 0.916 0.301 0.180 1.000* 1.000
GH3 0.693 -0.957 -1.000* -0.219 1.000** 0.649 0.549 0.911 0.921 1.000
PP2C -0.988 0.311 0.586 -0.674 -0.585 -0.995 -1.000* -0.185 -0.209 -0.573 1.000
ABF 0.504 0.541 0.258 0.998* -0.260 0.555 0.654 -0.646 -0.626 -0.274 -0.631 1.000
MPK6 0.425 -0.999* -0.942 -0.521 0.942 0.370 0.251 0.995 0.997* 0.947 -0.280 -0.568 1.000
EIN3 -0.998* 0.510 0.749 -0.497 -0.747 -0.992 -0.969 -0.394 -0.417 -0.738 0.976 -0.447 -0.482 1.000
BRI1 -0.992 0.336 0.607 -0.654 -0.606 -0.998* -0.998* -0.210 -0.235 -0.594 1.000* -0.611 -0.305 0.981 1.000
BKR1 0.299 -0.986 -0.888 -0.631 0.889 0.241 0.118 0.999* 0.998* 0.895 -0.148 -0.674 0.991 -0.360 -0.174 1.000
BIN2 0.992 -0.340 -0.611 0.650 0.610 0.998* 0.998* 0.215 0.240 0.598 -1.000* 0.607 0.309 -0.982 -1.000** 0.179 1.000
NPP1 0.349 -0.993 -0.911 -0.589 0.912 0.293 0.171 1.000* 1.000** 0.918 -0.200 -0.634 0.997 -0.409 -0.226 0.999* 0.231 1.000

Fig. 7

Protein network interaction pattern of differentially expressed genes a: folate synthesis protein and one-carbon metabolism key protein network analysis; b: folate synthesis protein and hormone conduction protein network analysis. DDM1(2): decrease in DNA methylation 1(2); MET1(3): methyltransferase 1(3); elfl8: elongation factor Tu; HSP90: heat shock protein 90; SGT1: suppressor of G2 allele of skp1; FLS2: FLAGELLIN SENSING 2; MPK6: Mitogen-activated protein kinase 6; MAPK1/3: Mitogen Activated Protein Kinase 1/3; COI1: coronatine-insensitive protein 1; EDS1: enhanced disease susceptibility 1 protein; RAR1: disease resistance protein. Light blue and pink lines: known interactions; Dark green, red, and blue lines: predicted interactions; Light green line: text mining; Black line: co-expression; Purple line: protein homology."

Table S8

Correlation analysis between the synthesis gene of folate and the key genes of methylation"

基因名称
Gene ID		 DHFR1 DHFR2 MET1 MET3 DDM1 DDM2
DHFR1 1
DHFR2 0.914 1
MET1 0.404 -0.002 1
MET3 0.916 1.000** 0.004 1
DDM1 0.970 0.788 0.614 0.791 1
DDM2 0.656 0.293 0.955 0.299 0.820 1

Table S9

Correlation analysis of folate synthesis gene with key genes of immune pathway and hormone signaling pathway"

基因名称Gene ID DHFR1 DHFR2 elfl8 SGT1 HSP90 FLS2 MPK6 MAPK1/3 COI1 EDS1 RAR1
DHFR1 1
DHFR2 0.914 1
elfl8 0.985 0.971 1
SGT1 0.731 0.945 0.839 1
HSP90 0.838 0.987 0.921 0.985 1
FLS2 0.636 0.894 0.761 0.992 0.954 1
MPK6 0.748 0.953 0.852 1.000* 0.989 0.988 1
MAPK1/3 0.574 0.857 0.708 0.978 0.927 0.997* 0.973 1
COI1 0.531 0.829 0.670 0.966 0.907 0.992 0.960 0.999* 1
EDS1 0.430 0.759 0.581 0.930 0.853 0.970 0.921 0.986 0.993 1
RAR1 0.622 0.886 0.749 0.989 0.948 1.000* 0.985 0.998* 0.994 0.974 1
[1] Young D W. The biosynthesis of the vitamins thiamin, riboflavin, and folic acid. Nat Prod Rep, 1986,3:395-419.
doi: 10.1039/np9860300395 pmid: 3547188
[2] Chistoserdova L, Vorholt J A, Thauer R K, Lidstrom M E. C1 transfer enzymes and coenzymes linking methylotrophic bacteria and methanogenic Archaea. Science, 1998,281:99-102.
doi: 10.1126/science.281.5373.99 pmid: 9651254
[3] 胡廷章. 生物体中一碳单位的代谢. 四川三峡学院学报, 1999, (5):74-77.
Hu Y Z. The metabolism of one carbon uni tin living. J Chongqing Three Gorges Univ, 1999, (5):74-77 (in Chinese with English abstract).
[4] Strum W, Nixon P F, Bertino J B, Bertino H J. Intestinal folate absorption: I. 5-methyltetrahydrofolic acid. J Investig, 1971,50:1910-1916.
[5] Shane B. Folylpolyglutamate synthesis and role in the regulation of one-carbon metabolism. Vitam Horm, 1989,45:263-335.
doi: 10.1016/s0083-6729(08)60397-0 pmid: 2688305
[6] 单齐冀. 糯玉米种质资源评价及籽粒发育过程叶酸合成代谢规律研究. 华南理工大学硕士学位论文, 广东广州, 2019.
Shan Q J. The Germplasm Evaluation and Folate Synthetic Metabolism during Kernel Development of Waxy Maize. MS Thesis of South China University of Technology, Guangzhou, Guangdong, China, 2019 (in Chinese with English abstract).
[7] Gorelova V, Bastien O, De Clerck O, Lespinats S, Rébeillé F, Van Der Straeten D. Evolution of folate biosynthesis and metabolism across algae and land plant lineages. Sci Rep, 2019,9:99-102.
doi: 10.1038/s41598-018-37471-0 pmid: 30643172
[8] 张大众, 刘佳佳, 冯佰利. 中国谷子种植利用史及其演进启示. 草业学报, 2018,27(3):173-186.
Zhang D Z, Liu J J, Feng B L. History of foxtail millet planting, husbandry, and societal utilization in China. Acta Pratacult Sin, 2018,27(3):173-186 (in Chinese with English abstract).
[9] 郑楠楠, 綦文涛, 王春玲, 贠婷婷, 幺杨, 任贵兴. 不同品种谷子营养成分及功能活性成分差异化分析. 粮油食品科技, 2018,26(2):34-39.
Zheng N N, Qi W T, Wang C L, Yun T T, Yao Y, Ren G X. Comparative analysis of nutritional and functional components in different kinds of millet. Sci Technol Cereals Oils Foods, 2018,26(2):34-39 (in Chinese with English abstract).
[10] Paul M F. Dietary Reference intakes for thiamin, riboflavin, niacin, vitamin B6, folate, vitamin B12, pantothenic acid, biotin and choline. Trends Food Sci Technol, 2000,11:296-297.
[11] 邵丽华. 山西省小米叶酸含量的研究. 山西师范大学硕士学位论文, 山西临汾, 2014.
Shao L H. Study of Folic Acid Content in Millet of Shanxi Province. MS Thesis of Shanxi Normal University, Linfen, Shanxi, China, 2014 (in Chinese with English abstract).
[12] 侯思宇, 宋敏, 闫陆飞, 孙朝霞, 韩渊怀, 李红英. HPLC法测定谷子籽粒叶酸含量及种质资源评价. 土壤, 2018,50:1235-1240.
Hou S Y, Song M, Yan L F, Sun Z X, Han Y H, Li H Y. Evaluation and detection of folic acid content of seeds among foxtail millet accessions by HPLC method. Soils, 2018,50:1235-1240 (in Chinese with English abstract).
[13] Blancquaert D, Van Daele J, Strobbe S, Kiekens F, Storozhenko S, De Steur H, Gellynck X, Lambert W, Stove C, Van Der Straeten D. Improving folate (vitamin B9) stability in biofortified rice through metabolic engineering. Nat Biotechnol, 2015,33:1076-1078.
[14] Jacob J A. Twenty years after folic acid fortification, FDA ponders expansion to corn masa flour. JAMA, 2016,315:1821-1822.
doi: 10.1001/jama.2016.2611 pmid: 27082432
[15] Mahato A, Vyas S, Chatterjee N S. HPLC-UV estimation of folic acid in fortified rice and wheat flour using enzymatic extraction and immunoaffinity chromatography enrichment: an interlaboratory validation study. J AOAC Int, 2020,103:73-77.
doi: 10.5740/jaoacint.19-0207 pmid: 31455468
[16] Storozhenko S, Brouwer D V, Volckaert M, Navarrete O, Blancquaert D, Zhang G F, Lambert W, Van Der Straeten D. Folate fortification of rice by metabolic engineering. Nat Biotechnol, 2007,25:1277-1279.
doi: 10.1038/nbt1351 pmid: 17934451
[17] 汪冉冉. 过量表达GCHI和ADCS基因对提高植物叶酸含量的研究. 兰州大学硕士学位论文, 甘肃兰州, 2013.
Wang R R. The Study on Enhancing Folate Content in Plants by Overexpression of GCHI and ADCS Genes. MS Thesis of Lanzhou University, Lanzhou, Gansu, China, 2013 (in Chinese with English abstract).
[18] Wan X, Han L D, Yang M, Zhang H Y, Zhang C Y, Hu P. Simultaneous extraction and determination of mono-polyglutamyl folates using high-performance liquid chromatography-tandem mass spectrometry and its applications in starchy crops. Analy Bioanaly Chem, 2019,411:2891-2904.
[19] Lian T, Guo W Z, Chen M R, Li J L, Liang Q J, Liu F, Meng H Y, Xu B S, Chen J F, Zhang C Y, Jiang L. Genome-wide identification and transcriptional analysis of folate metabolism-related genes in maize kernels. BMC Plant Biol, 2015,15:1-14.
[20] 韩娟英, 何曦, 蒋宙蕾, 梅沙, 张宁, 吴殿星. 富含叶酸水稻研究进展. 中国稻米, 2017,23(6):7-12.
Han J Y, He X, Jiang Z L, Mei S, Zhang N, Wu D X. Progress on high folate content rice. China Rice, 2017,23(6):7-12 (in Chinese with English abstract).
[21] Blancquaert D, Van Daele J, Storozhenko S, Stove C P, Lambert W, Van Der Straeten D. Rice folate enhancement through metabolic engineering has an impact on rice seed metabolism, but does not affect the expression of the endogenous folate biosynthesis genes. Plant Mol Biol, 2013,83:329-349.
doi: 10.1007/s11103-013-0091-7 pmid: 23771598
[22] Hanson A D, Gregory J F. Folate biosynthesis, turnover, and transport in plants. Annu Rev Plant Biol, 2011,62:105-125.
doi: 10.1146/annurev-arplant-042110-103819 pmid: 21275646
[23] Albani D, Parisi B, Carbonera D, Cella R. Dihydrofolate reductase from Daucus carota cell suspension cultures: purification, molecular and kinetic characterization. Plant Mol Biol, 1985,5:363-372.
pmid: 24306990
[24] Bachmann B, Follmann H. Deoxyribonucleotide biosynthesis in green algae: characterization of thymidylate synthase-dihydrofolate reductase in Scenedesmus obliquus. Arch Biochem Biophys, 1987,256:244-252.
doi: 10.1016/0003-9861(87)90442-5 pmid: 3606123
[25] Gorelova V, De Lepeleire J, Van Daele J, Pluim D, Meï C, Cuypers A, Leroux O, Rébeillé F, Schellens J H, Blancquaert D, Stove C P, Van Der Straeten D. Dihydrofolate reductase/ Thymidylate synthase fine-tunes the folate status and controls redox homeostasis in plants. Plant Cell, 2017,29:2831-2853.
doi: 10.1105/tpc.17.00433 pmid: 28939595
[26] Kim S E, Cole P D, Cho R C, Ly A, Ishiguro L, Sohn K J, Croxford R, Kamen B A, Kim Y I. γ-glutamyl hydrolase modulation and folate influence chemosensitivity of cancer cells to 5-fluorouracil and methotrexate. British J Cancer, 2013,109:2175-2188.
doi: 10.1038/bjc.2013.579
[27] Cheng C Y, Krishnakumar V, Chan A P, Thibaud N F, Schobel S, Town C D. Araport11: a complete reannotation of the Arabidopsis thaliana reference genome. Plant J, 2017,89:789-804.
doi: 10.1111/tpj.13415 pmid: 27862469
[28] Swarup R, Crespi M, Bennett M J. One gene, many proteins: mapping cell-specific alternative splicing in plants. Dev Cell, 2016,39:383-385.
doi: 10.1016/j.devcel.2016.11.002 pmid: 27875679
[29] Zhang X, Mount S M. Two alternatively spliced isoforms of the Arabidopsis SR45 protein have distinct roles during normal plant development. Plant Physiol, 2009,150:1450-1458.
doi: 10.1104/pp.109.138180 pmid: 19403727
[30] Airoldi C A, Bergonzi S, Davies B. Single amino acid change alters the ability to specify male or female organ identity. Proc Natl Acad Sci USA, 2010,107:18898-18902.
doi: 10.1073/pnas.1009050107 pmid: 20956314
[31] Severing E I, Dijk A D J V, Morabito G, Lange J B, Immink R G H, van Ham R C H J. Predicting the impact of alternative splicing on plant MADS domain protein function. PLoS One, 2012,7:e30524.
doi: 10.1371/journal.pone.0030524 pmid: 22295091
[32] Chen M X, Zhang K L, Gao B, Yang J A, Tian Y, Das D, Fan D, Dai L, Hao G, Yang G, Zhang J H, Zhu F, Fang Y M. Phylogenetic comparison of 5′ splice site determination in central spliceosomal proteins of the U1-70K gene family, in response to developmental cues and stress conditions. Plant J, 2020,103:357-378.
doi: 10.1111/tpj.14735 pmid: 32133712
[33] González B, Vera P. Folate metabolism interferes with plant immunity through 1C methionine synthase-directed genome-wide DNA methylation enhancement. Mol Plant, 2019,12:1227-1242.
pmid: 31077872
[34] John S, Fabrice R, John F. Folic acid and folates: the feasibility for nutritional enhancement in plant foods. J Sci Food Agric, 2000,80:795-824.
[35] Wang L, Kong D D, Lyu Q, Niu G Q, Han T T, Zhao X C, Meng S L, Cheng Q, Guo S C, Du J, Wu Z L, Wang J Z, Bao F, Hu Y, Pan X J, Xia J C, Yuan D, Han L D, Lian T, Zhang C Y, Wang H Y, He X J, He Y K. Tetrahydrofolate modulates floral transition through epigenetic silencing. Plant Physiol, 2017,174:1274-1284.
doi: 10.1104/pp.16.01750 pmid: 28450424
[36] Finni W, Basem K, Marion W, Thomas H, Katrin F O, Wilfried S, Philippe S K, Corina V A. Folic acid induces salicylic acid-dependent immunity in Arabidopsis and enhances susceptibility to Alternaria brassicicola. Mol Plant Pathol, 2015,16:616-622.
doi: 10.1111/mpp.12216 pmid: 25348251
[37] 肖熙鸥, 林文秋, 李可, 冯雪峰, 李威, 邹华芬, 金辉. 茄子Enhanced disease susceptibility 1互作蛋白的初步筛选与分析. 热带作物学报, 2018,39:1580-1584.
Xiao X O, Lin W Q, Li K, Feng X F, Li W, Zou H F, Jin H. Screening and analysis of candidate interactors of enhanced disease susceptibility 1 (SmEDS1) in eggplant. Chin J Trop Crops, 2018,39:1580-1584 (in Chinese with English abstract).
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