Abstract
Glyceollins (Glys), produced by soy plants in response to stress, have anti-estrogenic activity in breast and ovarian cancer cell lines in vitro and in vivo. In addition to known anti-estrogenic effects, Gly exhibits mechanisms of action not involving estrogen receptor (ER) signaling. To date, effects of Gly on gene expression in the brain are unknown. For this study, we implanted 17-β estradiol (E2) or placebo slow-release pellets into ovariectomized CFW mice followed by 11 days of exposure to Gly or vehicle i.p. injections. We then performed a microarray on total RNA extracted from whole-brain hemispheres and identified differentially expressed genes (DEGs) by a 2 × 2 factorial ANOVA with an false discovery rate (FDR) = 0.20. In total, we identified 33 DEGs with a significant E2 main effect, 5 DEGs with a significant Gly main effect, 74 DEGs with significant Gly and E2 main effects (but no significant interaction term), and 167 DEGs with significant interaction terms. Clustering across all DEGs revealed that transcript abundances were similar between the E2 + Gly and E2-only treatments. However, gene expression after Gly-only treatment was distinct from both of these treatments and was generally characterized by higher transcript abundance. Collectively, our results suggest that whether Gly acts in the brain through ER-dependent or ER-independent mechanisms depends on the target gene.
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Acknowledgments
We thank the University of Louisville Microarray Core staff (Sabine Waigel, Vennila Arumugam) for technical assistance and the Research Resources Facility staff for animal care. This work was supported by a Career Development Award to CC by the Center for Environmental Genomics and Integrative Biology, which was funded by National Institutes of Health/NIEHS grant #P30ES014443, a Research Incentive Grant from UofL to CC and CMK, and an AREA award from the Kentucky Biomedical Research Infrastructure Network, which was funded by NIGMS grant #P20GM103436. RBP was supported by the Wallace Endowment to the University of Louisville Department of Biology. The University of Virginia Center for Research in Reproduction Ligand Assay and Analysis Core is supported by the Eunice Kennedy Shriver NICHD/NIH (SCCPIR) Grant U54-HD28934.
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Online Resource 1
Genes measured with RT-qPCR and assay IDs for TaqMan primers (Life Technologies) (PDF 8 kb)
Online Resource 2
Interaction plots showing representative patterns of expression of DEGs. a–b) DEGs with main E2 effect only, no interaction effect (n = 33); c-d) DEGs with main Gly effect only, no interaction effect (n = 5); e-h) DEGs with main E2 and main Gly effects, no interaction effect (n = 74); e) Gly up, E2 down; f) Gly down, E2 up; g) Gly up, E2 up; h) Gly down, E2 down;i) One of the 20 significant interaction effects patterns, which indicates possible E2 opposition of Gly effects on gene expression (65 of 167 DEGs, see text). ❍ = Con, ● = E2, △ = Gly, ▲ = E2 + Gly. Left columns (circles) = no Gly; right columns (triangles) = Gly. Dotted lines connect the non-E2 groups (❍ and △), solid lines connect the two E2 groups (● and ▲) (PDF 19 kb)
Online Resource 3
All DEGs with significant main E2 effect only, no interaction term (PDF 106 kb)
Online Resource 4
All DEGs with significant main Gly effect only, no interaction term (PDF 93 kb)
Online Resource 5
All DEGs with significant Gly and significant E2 main effects, no interaction term (PDF 141 kb)
Online Resource 6
All DEGs with significant E2 x Glyinteraction effects (PDF 146 kb)
Online Resource 7
RT-qPCRstatistical outcomes (PDF 13 kb)
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Bamji, S.F., Page, R.B., Patel, D. et al. Soy glyceollins regulate transcript abundance in the female mouse brain. Funct Integr Genomics 15, 549–561 (2015). https://doi.org/10.1007/s10142-015-0442-3
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DOI: https://doi.org/10.1007/s10142-015-0442-3