journal article·2025-11Open access

Marker Metabolite‐Based Multi‐Omics Analysis Identifies New Loci Controlling Thousand Seed Weight in Brassica Napus

Long Li, Zhitao Tian, Xiaowei Wu, Zengdong Tan, Xu Han, Yuyan Xiang, Jie Chen, Hu Zhao, Wei Chen, Liang Guo, Xuan Yao

Advanced Science· Volume 12· Issue 42

Abstract

Abstract The thousand seed weight (TSW) of Brassica napus ( B. napus ) is a crucial agronomic trait, and metabolites serve as a bridge between genotype and phenotype. Leveraging previously released metabolome data from a 388 B. napus population, 137 marker metabolites significantly correlated with TSW are identified, and 21 markers significantly correlate with both TSW and seed oil content. To delve deeper into the relationships between selected marker metabolites and phenotypes, a comprehensive multi‐omics analysis is conducted, which unveils 734 metabolite‐QTLs, 5,225 expression‐QTLs, and 9,077 transcriptome‐wide significantly associated genes. Employing weighted correlation network analysis, a triple‐link network is constructed involving 133 metabolites, 731 QTLs, and 3,321 genes. The multi‐omics analysis highlights the impact of the transcriptional factor TGACG motif‐binding factor 6 (BnaA03.TGA6) on TSW. Functional validation experiments with tga6 CRISPR/Cas9 mutants demonstrate larger seeds and altered associated metabolites levels compared to the wild type. RNA Sequencing, dual‐luciferase assay, Cleavage Under Targets and Tagmentation, and Electrophoretic Mobility Shift Assay results confirm that BnaA03.TGA6 activates the expression of E3 ubiquitin‐protein ligase DA2 . In conclusion, this study is the first marker metabolite‐based multi‐omics analysis in unraveling the genetic basis of TSW in crop and identifying BnaA03.TGA6 as a novel gene negatively influencing TSW in B. napus .
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Lipid metabolism and biosynthesis · Genetic Mapping and Diversity in Plants and Animals · Nitrogen and Sulfur Effects on Brassica

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

National Key Laboratory of Crop Genetic Improvement Hubei Hongshan Laboratory Huazhong Agricultural University Wuhan 430070 China

Department of Microbiology Harvard Medical School Boston MA 02115 USA

Yazhouwan National Laboratory Sanya 572025 China

Funding

National Science Fund for Distinguished Young Scholars

References (100+)

Show source-provided references
1. 10.1038/nrm145110.1038/nrm1451 ↗
2. 10.1016/j.tplants.2020.12.00810.1016/j.tplants.2020.12.008 ↗
3. 10.1073/pnas.060970910410.1073/pnas.0609709104 ↗
4. 10.1073/pnas.090347810610.1073/pnas.0903478106 ↗
5. 10.1021/ac051495j10.1021/ac051495j ↗
6. 10.1104/pp.111.18844110.1104/pp.111.188441 ↗
7. 10.1104/pp.110.16303010.1104/pp.110.163030 ↗
8. 10.1007/s00018-012-1091-510.1007/s00018-012-1091-5 ↗
9. 10.1146/annurev-arplant-043014-11472010.1146/annurev-arplant-043014-114720 ↗
10. 10.1073/pnas.112081310910.1073/pnas.1120813109 ↗
11. 10.1016/j.cj.2019.04.00210.1016/j.cj.2019.04.002 ↗
12. 10.1186/s13059-023-02984-z10.1186/s13059-023-02984-z ↗
13. 10.1146/annurev-genet-120116-02464010.1146/annurev-genet-120116-024640 ↗
14. 10.1016/j.micpath.2018.08.03410.1016/j.micpath.2018.08.034 ↗
15. 10.1093/mp/sst08010.1093/mp/sst080 ↗
16. 10.1038/ng.300710.1038/ng.3007 ↗
17. 10.1038/ncomms443810.1038/ncomms4438 ↗
18. 10.1016/j.cell.2017.12.01910.1016/j.cell.2017.12.019 ↗
19. 10.1152/physiolgenomics.00127.201310.1152/physiolgenomics.00127.2013 ↗
20. 10.1007/978-1-4939-2444-8_1310.1007/978-1-4939-2444-8_13 ↗
21. 10.1073/pnas.142324411210.1073/pnas.1423244112 ↗
22. 10.1111/tpj.1423610.1111/tpj.14236 ↗
23. 10.1016/j.molp.2018.03.00510.1016/j.molp.2018.03.005 ↗
24. 10.1016/j.ajhg.2022.12.01110.1016/j.ajhg.2022.12.011 ↗
25. 10.1038/s41576-019-0127-110.1038/s41576-019-0127-1 ↗
26. 10.3389/fpls.2023.126851110.3389/fpls.2023.1268511 ↗
27. 10.1007/s00122-025-04923-x10.1007/s00122-025-04923-x ↗
28. 10.1038/s41477-020-00816-710.1038/s41477-020-00816-7 ↗
29. 10.1105/tpc.15.0020810.1105/tpc.15.00208 ↗
30. 10.1111/pbi.1333510.1111/pbi.13335 ↗
31. 10.1093/mp/sst08010.1093/mp/sst080 ↗
32. 10.1186/s13059-022-02719-610.1186/s13059-022-02719-6 ↗
33. 10.1111/tpj.1599510.1111/tpj.15995 ↗
34. 10.1111/tpj.1495010.1111/tpj.14950 ↗
35. 10.1038/ng.350610.1038/ng.3506 ↗
36. 10.1038/s41588-018-0092-110.1038/s41588-018-0092-1 ↗
37. 10.1016/j.molp.2020.12.00310.1016/j.molp.2020.12.003 ↗
38. 10.1111/nph.1646810.1111/nph.16468 ↗
39. 10.1111/nph.1732510.1111/nph.17325 ↗
40. 10.1038/nature0143410.1038/nature01434 ↗
41. 10.4141/cjps78-10810.4141/cjps78-108 ↗
42. 10.1105/tpc.113.11506310.1105/tpc.113.115063 ↗
43. 10.1111/j.1469-8137.2012.04083.x10.1111/j.1469-8137.2012.04083.x ↗
44. 10.1111/j.1365-313X.2011.04638.x10.1111/j.1365-313X.2011.04638.x ↗
45. 10.1111/tpj.1397110.1111/tpj.13971 ↗
46. 10.1111/tpj.1373710.1111/tpj.13737 ↗
47. 10.1105/tpc.17.0095910.1105/tpc.17.00959 ↗
48. 10.1016/j.molp.2018.04.00410.1016/j.molp.2018.04.004 ↗
49. 10.1111/tpj.1302510.1111/tpj.13025 ↗
50. 10.1104/pp.106.08884910.1104/pp.106.088849 ↗
51. 10.1111/pbi.1269610.1111/pbi.12696 ↗
52. 10.1146/annurev-arplant-050718-09585110.1146/annurev-arplant-050718-095851 ↗
53. 10.1093/jxb/eru54910.1093/jxb/eru549 ↗
54. 10.1371/journal.pbio.300093010.1371/journal.pbio.3000930 ↗
55. 10.1105/tpc.104.02713610.1105/tpc.104.027136 ↗
56. 10.1186/s12870-023-04267-y10.1186/s12870-023-04267-y ↗
57. 10.1105/tpc.18.0057710.1105/tpc.18.00577 ↗
58. 10.1073/pnas.161176311310.1073/pnas.1611763113 ↗
59. 10.1111/j.1365-313X.2007.03268.x10.1111/j.1365-313X.2007.03268.x ↗
60. 10.1094/MPMI-04-12-0078-IA10.1094/MPMI-04-12-0078-IA ↗
61. 10.1073/pnas.96.11.652310.1073/pnas.96.11.6523 ↗
62. 10.1105/tpc.01489410.1105/tpc.014894 ↗
63. 10.1093/plcell/koac01510.1093/plcell/koac015 ↗
64. 10.1021/acs.jnatprod.1c0039910.1021/acs.jnatprod.1c00399 ↗
65. 10.1242/dev.0219410.1242/dev.02194 ↗
66. 10.1023/A:101520711411710.1023/A:1015207114117 ↗
67. 10.1111/j.1365-313X.2005.02426.x10.1111/j.1365-313X.2005.02426.x ↗
68. 10.1016/j.bbaexp.2007.01.01010.1016/j.bbaexp.2007.01.010 ↗
69. 10.1111/tpj.1295710.1111/tpj.12957 ↗
70. 10.1105/tpc.106.04726610.1105/tpc.106.047266 ↗
71. 10.1105/tpc.114.12266310.1105/tpc.114.122663 ↗
72. 10.1093/nar/gkad105910.1093/nar/gkad1059 ↗
73. 10.1038/ng.359610.1038/ng.3596 ↗
74. 10.1038/ng.101810.1038/ng.1018 ↗
75. 10.1038/ng.69510.1038/ng.695 ↗
76. 10.1111/j.1365-313X.2007.03383.x10.1111/j.1365-313X.2007.03383.x ↗
77. 10.1186/1471-2229-14-11410.1186/1471-2229-14-114 ↗
78. 10.1007/s00122-020-03553-910.1007/s00122-020-03553-9 ↗
79. 10.1111/pbi.1287210.1111/pbi.12872 ↗
80. 10.1111/nph.1363210.1111/nph.13632 ↗
81. 10.1007/s00122-006-0323-110.1007/s00122-006-0323-1 ↗
82. 10.1186/1471-2156-13-10510.1186/1471-2156-13-105 ↗
83. 10.3389/fpls.2018.0092110.3389/fpls.2018.00921 ↗
84. Lu K. · Front. Plant. Sci. · 2017 · 8 · 206
85. 10.3390/metabo802003110.3390/metabo8020031 ↗
86. 10.1002/mas.2153510.1002/mas.21535 ↗
87. 10.1016/j.copbio.2018.07.01010.1016/j.copbio.2018.07.010 ↗
88. 10.1126/science.125343510.1126/science.1253435 ↗
89. 10.1086/51979510.1086/519795 ↗
90. 10.1038/srep0687410.1038/srep06874 ↗
91. 10.1038/nbt.230210.1038/nbt.2302 ↗
92. 10.1186/1471-2105-9-55910.1186/1471-2105-9-559 ↗
93. 10.1016/j.ab.2013.02.02810.1016/j.ab.2013.02.028 ↗
94. 10.1007/s11032-020-01174-010.1007/s11032-020-01174-0 ↗
95. 10.1038/nprot.2007.19910.1038/nprot.2007.199 ↗
96. 10.1093/bioinformatics/btw35410.1093/bioinformatics/btw354 ↗
97. 10.1093/bioinformatics/bts63510.1093/bioinformatics/bts635 ↗
98. 10.1186/1471-2105-12-32310.1186/1471-2105-12-323 ↗
99. 10.1186/s13059-014-0550-810.1186/s13059-014-0550-8 ↗
100. Han X. · Plant Cell · 2025 · 37