journal article·2026-05Open access

Genetic Control of Tissue Remodeling by a Non‐Coding SNP in ITGA8 Explains Carotenoid‐Based Color Polymorphism in Marine Mollusks

Xiaohui Wei, Wenzhu Peng, Zekun Huang, Bingye Yang, Yiyu Wu, Minzhen Han, Yi Wang, Bingling Peng, Guosheng Hu, Jiawei Hong, Guijia Liu, Linwei Nie, Yang Gan, Miaoqin Huang, Xuan Luo, Weiwei You, Wen Liu, Caihuan Ke

Advanced Science· Volume 13· Issue 27

Abstract

ABSTRACT Biological color polymorphism represents a widespread form of phenotypic diversity in nature; however, the multi‐scale regulatory mechanisms of carotenoid‐based coloration, from genetic control to cell states and tissue architecture, remain poorly understood. Here, using the orange‐muscle giant abalone ( Haliotis gigantea ) as a model, we demonstrate that a non‐coding single nucleotide polymorphism (SNP) in the integrin subunit alpha 8 ( ITGA8 ) gene induces alternative splicing, thereby promoting carotenoid‐based coloration through the regulation of tissue remodeling processes, including cell state transitions and reconstruction of the physical microenvironment. Through chromosome‐level genome assembly and genome‐wide association studies (GWAS), we identified a SNP that disrupts the interaction between ITGA8 pre‐mRNA and the splicing factor ILF2, leading to altered ITGA8 splicing. Multi‐omics analyses and transmission electron microscopy reveal that these splicing changes promote carotenoid accumulation in abalone muscle by weakening cell adhesion and enlarging intercellular spaces. Functional validation supports the role of ITGA8 in regulating cell adhesion and carotenoid uptake. Our study uncovers a cross‐scale mechanism by which genetic variation drives macroscopic phenotypic diversity in carotenoid‐based color polymorphism through the regulation of cell state transitions and tissue remodeling. These findings provide novel insights into the genotype‐phenotype relationship and advance our understanding of color diversity in multicellular organisms.
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Research context

OpenAlex topics: Antioxidant Activity and Oxidative Stress · Algal biology and biofuel production · Calcium Carbonate Crystallization and Inhibition

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

Affiliations

State Key Laboratory of Mariculture Breeding College of Ocean and Earth Sciences Xiamen University Xiamen China

Institutes of Brain Science State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science Fudan University Shanghai China

Fujian Ocean Innovation Center Xiamen China

College of the Environmental and Ecology Xiamen University Xiamen China

Xiamen Key Laboratory of Marine Medicinal Natural Products Resources Xiamen Medical College Xiamen China

State Key Laboratory of Cellular Stress Biology School of Pharmaceutical Sciences Faculty of Medicine and Life Sciences Xiamen University Xiamen China

State Key Laboratory of Marine Environmental Science College of Ocean and Earth Sciences Xiamen University Xiamen China

Funding

National Key Research and Development Program of China · Earmarked Fund for China Agriculture Research System

References (79)

Show source-provided references
1. 10.1126/science.aan022110.1126/science.aan0221 ↗
2. 10.1098/rstb.2016.053610.1098/rstb.2016.0536 ↗
3. 10.1098/rsif.2008.0459.focus10.1098/rsif.2008.0459.focus ↗
4. 10.1093/jhered/esab01510.1093/jhered/esab015 ↗
5. 10.1111/bij.1281010.1111/bij.12810 ↗
6. 10.1098/rspb.2019.058910.1098/rspb.2019.0589 ↗
7. 10.1016/j.asd.2016.11.00610.1016/j.asd.2016.11.006 ↗
8. 10.1038/s41467-020-15033-110.1038/s41467-020-15033-1 ↗
9. 10.1111/nph.1522910.1111/nph.15229 ↗
10. 10.1038/s41467-018-02974-x10.1038/s41467-018-02974-x ↗
11. Carotenoid Pigments: Their Possible Role in Protecting Against Photooxidation in Eyes and Photoreceptor Cells
12. 10.1016/0305-0491(90)90171-O10.1016/0305-0491(90)90171-O ↗
13. 10.1073/pnas.170075111410.1073/pnas.1700751114 ↗
14. 10.1016/j.bbalip.2019.03.00310.1016/j.bbalip.2019.03.003 ↗
15. 10.1016/j.foodchem.2013.09.07810.1016/j.foodchem.2013.09.078 ↗
16. 10.1002/biof.552010020310.1002/biof.5520100203 ↗
17. 10.1002/mnfr.20060014510.1002/mnfr.200600145 ↗
18. 10.1073/pnas.16218289910.1073/pnas.162182899 ↗
19. 10.1038/srep0193210.1038/srep01932 ↗
20. 10.1093/cvr/cvx22610.1093/cvr/cvx226 ↗
21. 10.1161/01.ATV.15.5.55110.1161/01.ATV.15.5.551 ↗
22. 10.1016/j.jtbi.2011.05.03010.1016/j.jtbi.2011.05.030 ↗
23. 10.1038/s41586-023-06052-110.1038/s41586-023-06052-1 ↗
24. 10.1242/dev.110.1.110.1242/dev.110.1.1 ↗
25. 10.1007/BF0270314910.1007/BF02703149 ↗
26. Cook P. A. · 2023
27. 10.3390/fishes809044310.3390/fishes8090443 ↗
28. 10.1111/are.1424810.1111/are.14248 ↗
29. 10.1155/2022/928759410.1155/2022/9287594 ↗
30. 10.1002/path.157110.1002/path.1571 ↗
31. 10.1159/00048816010.1159/000488160 ↗
32. 10.1242/jcs.108.2.53710.1242/jcs.108.2.537 ↗
33. 10.1016/j.ccell.2017.05.01110.1016/j.ccell.2017.05.011 ↗
34. 10.1210/edrv-17-3-20710.1210/edrv-17-3-207 ↗
35. 10.1016/S0092-8674(02)00971-610.1016/S0092-8674(02)00971-6 ↗
36. 10.1016/j.cub.2016.03.07610.1016/j.cub.2016.03.076 ↗
37. 10.1002/(SICI)1521-1878(199906)21:6<499::AID-BIES6>3.0.CO;2-D10.1002/(SICI)1521-1878(199906)21:6<499::AID-BIES6>3.0.CO;2-D ↗
38. 10.1038/nrm.2016.13910.1038/nrm.2016.139 ↗
39. 10.1038/s41467-020-16938-710.1038/s41467-020-16938-7 ↗
40. 10.1186/s13059-025-03539-010.1186/s13059-025-03539-0 ↗
41. 10.1016/j.tree.2019.12.00210.1016/j.tree.2019.12.002 ↗
42. 10.1016/j.abb.2015.01.01610.1016/j.abb.2015.01.016 ↗
43. 10.1038/s41592-019-0669-310.1038/s41592-019-0669-3 ↗
44. 10.1093/bioinformatics/btp32410.1093/bioinformatics/btp324 ↗
45. 10.1371/journal.pone.011296310.1371/journal.pone.0112963 ↗
46. 10.1093/bioinformatics/btm07110.1093/bioinformatics/btm071 ↗
47. 10.1093/bioinformatics/btv35110.1093/bioinformatics/btv351 ↗
48. 10.1038/nbt.272710.1038/nbt.2727 ↗
49. 10.1093/bib/bbm04810.1093/bib/bbm048 ↗
50. 10.1093/nar/gkm28610.1093/nar/gkm286 ↗
51. 10.1093/bioinformatics/bti101810.1093/bioinformatics/bti1018 ↗
52. 10.1073/pnas.192104611710.1073/pnas.1921046117 ↗
53. 10.1111/1755-0998.1343910.1111/1755-0998.13439 ↗
54. 10.1038/nbt.188310.1038/nbt.1883 ↗
55. 10.1038/nature1169610.1038/nature11696 ↗
56. 10.1098/rstb.2020.016410.1098/rstb.2020.0164 ↗
57. 10.1093/gigascience/gix05910.1093/gigascience/gix059 ↗
58. 10.1038/s41559-017-012110.1038/s41559-017-0121 ↗
59. 10.1016/j.aquaculture.2021.73784210.1016/j.aquaculture.2021.737842 ↗
60. 10.1126/science.287.5461.218510.1126/science.287.5461.2185 ↗
61. 10.1038/s41559-020-1156-z10.1038/s41559-020-1156-z ↗
62. 10.1101/gr.244830.11810.1101/gr.244830.118 ↗
63. 10.1186/gb-2008-9-1-r710.1186/gb-2008-9-1-r7 ↗
64. 10.1016/j.xinn.2021.10014110.1016/j.xinn.2021.100141 ↗
65. 10.1111/1755-0998.1356210.1111/1755-0998.13562 ↗
66. 10.1093/gigascience/giab00810.1093/gigascience/giab008 ↗
67. 10.1002/0471250953.bi1110s4310.1002/0471250953.bi1110s43 ↗
68. 10.1093/bioinformatics/btr33010.1093/bioinformatics/btr330 ↗
69. 10.1093/bioinformatics/bty87510.1093/bioinformatics/bty875 ↗
70. 10.1086/51979510.1086/519795 ↗
71. 10.1038/ng.231010.1038/ng.2310 ↗
72. 10.1086/31950110.1086/319501 ↗
73. 10.1016/j.cell.2017.08.01610.1016/j.cell.2017.08.016 ↗
74. Wu W. · 2021
75. 10.1038/nmeth.439610.1038/nmeth.4396 ↗
76. 10.1186/1471-2105-15-18210.1186/1471-2105-15-182 ↗
77. 10.1071/RDv30n1Ab10910.1071/RDv30n1Ab109 ↗
78. 10.1038/nbt.312210.1038/nbt.3122 ↗
79. 10.1371/journal.pone.015702210.1371/journal.pone.0157022 ↗