WNT4 Regulates Cellular Metabolism via Intracellular Activity at the Mitochondria in Breast and Gynecologic Cancers

Abstract

Wnt ligand WNT4 is critical in female reproductive tissue development, with WNT4 dysregulation linked to related pathologies including breast cancer (invasive lobular carcinoma, ILC) and gynecologic cancers. WNT4 signaling in these contexts is distinct from canonical Wnt signaling yet inadequately understood. We previously identified atypical intracellular activity of WNT4 (independent of Wnt secretion) regulating mitochondrial function, and herein examine intracellular functions of WNT4. We further examine how convergent mechanisms of WNT4 dysregulation impact cancer metabolism. In ILC, WNT4 is co-opted by estrogen receptor α (ER) via genomic binding in WNT4 intron 1, while in gynecologic cancers, a common genetic polymorphism (rs3820282) at this ER binding site alters WNT4 regulation. Using proximity biotinylation (BioID), we show canonical Wnt ligand WNT3A is trafficked for secretion, but WNT4 is localized to the cytosol and mitochondria. We identified DHRS2, mTOR, and STAT1 as putative WNT4 cytosolic/mitochondrial signaling partners. Whole metabolite profiling, and integrated transcriptomic data, support that WNT4 mediates metabolic reprogramming via fatty acid and amino acid metabolism. Furthermore, ovarian cancer cell lines with rs3820282 variant genotype are WNT4 dependent and have active WNT4 metabolic signaling. In protein array analyses of a cohort of 103 human gynecologic tumors enriched for patient diversity, germline rs3820282 genotype is associated with metabolic remodeling. Variant genotype tumors show increased AMPK activation and downstream signaling, with the highest AMPK signaling activity in variant genotype tumors from non-White patients. Taken together, atypical intracellular WNT4 signaling, in part via genetic dysregulation, regulates the distinct metabolic phenotypes of ILC and gynecologic cancers.

Significance: WNT4 regulates breast and gynecologic cancer metabolism via a previously unappreciated intracellular signaling mechanism at the mitochondria, with WNT4 mediating metabolic remodeling. Understanding WNT4 dysregulation by estrogen and genetic polymorphism offers new opportunities for defining tumor biology, precision therapeutics, and personalized cancer risk assessment.

FIGURE 1

BioID supports WNT4 localization to the mitochondria. A, Proteins enriched in HT1080 Wnt-BirA versus parental HT1080 cells lacking BirA construct expression. B, Overlap of proteins identified in HT1080 (A) versus HT1080-PKO and MM134 identifies n = 72 “high-confidence” WNT4-associated proteins. C, Gene ontology analysis for cellular compartment for WNT3A- versus WNT4-associated proteins. Dashed line = 1.3 (P = 0.05). D, Network analysis of WNT3A- versus WNT4-associated proteins via subcell barcode. Enrichments against cell line HCC287 background shown; parallel results observed with other cell line background data, for example, MCF7. E, Proteins with predicted cytosolic or mitochondrial localization (subcell barcode) among “high-confidence” WNT4-associated proteins. Red = predicted mitochondrial localization, pink = mTOR complex in mitochondrial dynamics, biogenesis, and autophagy. F, Biotin treatment and streptavidin pulldown was performed as for MS studies, and candidate WNT4-associated proteins from E detected by immunoblotting. Total protein by Ponceau.

FIGURE 2

ER regulates glycolysis, oxidative phosphorylation, and fatty acid metabolism in ILC cells. A, Overall metabolomics study design in MDA MB 134VI ILC cells; all samples in biological triplicate. B, Joint analysis of transcriptome + metabolome data identifies dysregulated pathways after ER knockdown. Transcriptome data from GSE171364. C, Metabolites levels altered by ER knockdown (left, n = 63), with gene expression changes associated with major metabolic mechanisms (right).

FIGURE 3

Metabolic effects of WNT4 knockdown mirror ER knockdown but have expanded impact on fatty acid and amino acid metabolic pathways. A, Metabolite levels altered by WNT4 knockdown (n = 77); pink text indicates a shared affected metabolite with ER knockdown. B, Metabolites dysregulated by WNT4 versus ER knockdown are strongly enriched for overlap. C, WNT4 versus ER knockdown effects on metabolite levels are strongly directly correlated, suggesting an overall parallel effect on ILC cell metabolism. D, Pathway analysis with ER metabolites (C) and WNT4 metabolites (Fig. 4A); P < 0.4 in ER and/or WNT4 dataset shown.

FIGURE 4

WNT4 knockdown impairs respiration but not glycolysis. A, Seahorse MitoStress test in MM134. Points represent mean of 6 biological replicates ± SD. B, Basal respiration is reduced by ER or WNT4 knockdown, but WNT4 knockdown impairs respiratory capacity. C, WNT4 knockdown suppresses OCR, that is, respiration (from B), but has a minimal effect on ECAR, that is, glycolysis. D, From metabolomics data, cellular lactic acid levels are reduced by ER knockdown, but not WNT4 knockdown. B–D, comparisons by ANOVA with Dunnett correction. *, adj.P < 0.05. E, Reduction in lactic acid production was also observed in lactate levels in conditioned medium. *, Two-way ANOVA siRNA effect P < 0.05. F and G, DEMETER2 scores for n = 15 genes in pyruvate metabolism [Kyoto Encyclopedia of Genes and Genomes (KEGG) M00001 and M00307] normalized as Z-scores for ER⁺ breast cancer cell lines. DEMETER2 siRNA used as ER⁺ ILC cell line data are not available in Depmap CRISPR-based screens. CAMA1 denoted separately as an “ILC-like” cell line. F, Total sum of Z-scores for the geneset per cell line. G, Hierarchal clustering for gene Z-scores indicates ER⁺ ILC lines as an independent cluster. Pyruvate metabolism genes at left, representative TCA genes at right.

FIGURE 5

WNT4 overexpression rescues some metabolic effects of ER:WNT4 pathway inhibition. A, Small-molecule inhibitor targets in current model of ER:WNT4 signaling pathway. B, Meta-analysis of parental MM134 cells versus WNT4-OE MM134 across drug treatment series (i.e., WNT4 effect controlled for inhibitor effect) identifies n = 71 metabolite levels altered by WNT4 overexpression. Green = overlap with siWNT4 dysregulated metabolites. C, Overall changes in metabolite levels caused by WNT4 knockdown versus WNT4 overexpression are inversely correlated, supporting regulation of associated metabolic pathways by WNT4. D, Inhibitor effects in parental MM134 versus WNT4-OE MM134 are strongly correlated, but a subset of inhibitor effects are reversed by WNT4 overexpression. E, Metabolites for which inhibitor effects is decreased by ≥30% for at least two inhibitors (n = 39).

FIGURE 6

Consensus WNT4-regulated metabolites are enriched for fatty acid metabolism. A, Consensus of n = 41 metabolites rescued with WNT4-OE and metabolites inversely regulated by WNT4-OE versus WNT4 knockdown. WNT4 ON versus OFF corresponds to differential regulation by WNT4 overexpression versus knockdown, respectively. B, Pathway analysis of consensus WNT4-regulated metabolites. C, Gene set enrichment analysis of genes differentially expressed in Luminal A ILC versus Luminal A IDC (n = 1360) includes metabolic pathways, with relative gene expression levels in ILC consistent with increased lipid metabolism and decreased glycolysis and OXPHOS. Points = individual genes (fold changes from TCGA, ref. 7); red bar = median fold change. D, fGSEA = fast gene set enrichment analysis of representative pathways in C. NES = normalized enrichment score.

FIGURE 7

WNT4 variant genotype is associated with active WNT4 signaling and metabolic remodeling in ovarian cancer cells. A, Left, Proliferation assessed by dsDNA quantification 6 days post-transfection with siRNA (siNT = non-targeting control pool). Bars represent mean of 6 biological replicates ± SD; *, P < 0.05, siWNT4 versus siNT, t test with Welch correction. WT versus Var model comparison by t test of mean fold changes for siWNT4 versus siNT. Right, WNT4 mRNA by qPCR in parallel samples, 72 hours after siRNA. Mean of biological triplicate, fold change versus siNT. B, Lysates harvested 72 hours posttransfection. MCL1 levels assessed by densitometry and normalized to total S6 loading control (6). Fold change in normalized MCL1 levels in Variant versus WT genotype cell lines compared by Student t test. C, Metabolites dysregulated 72 hours after WNT4 knockdown (n = 44), as for MM134. D, Fold changes in lipid metabolites. Dashed line on y-axis: FDR = 0.2. E, Pathway enrichment (Metaboanalyst) for metabolites shown in C; K = KEGG, S = SMPDB.

FIGURE 8

WNT4 variant genotype is associated with metabolic remodeling in gynecologic tumors, mirroring ILC signaling. A–C, Points represent total network score for the listed RPPA targets for individual tumors based on rs3820282 genotype. Red bar = median score; P-value from Mann–Whitney t test. A, Tumor scores for AMPK-associated network. B, Tumor scores for phospho-AMPK only. C, Tumor scores for glucose metabolism-associated network. D, Scatterplot of network scores in A and C; correlation from full dataset, that is, WT and Var samples combined. E–G, Network scores as in A–C from n = 58 stage I endometrioid-type endometrial tumors; networks generated independently from full dataset. E, Tumor scores for metabolic signaling network associated with AMPK and lipid metabolism signaling. F, Tumor scores for phospho-AMPKα1 only. G, Tumor scores for glucose metabolism-associated network. H, Points represent AMPK network score for individual tumors based on rs3820282 genotype and race/ethnicity groups. *, adj.P < 0.1, ANOVA with Welch correction. Cells were transfected with siRNA for 72 hours prior to immunoblotting, with ovarian cancer cell lines (I) and ILC cell lines (J). Representative band from total protein staining shown as loading control.