Wnt/β-catenin signaling regulates amino acid metabolism through the suppression of cebpa and foxa1 in liver cancer cells

ABSTRACT Deregulation of the Wnt/β-catenin pathway is associated with the development of human cancer including colorectal and liver cancer. Although we previously showed that histidine

ammonia lyase (_HAL_) was transcriptionally reduced by the β-catenin/TCF complex in liver cancer cells, the mechanism(s) of its down-regulation by the complex remain to be clarified. In this

study, we search for the transcription factor(s) regulating _HAL_, and identify CEBPA and FOXA1, two factors whose expression is suppressed by the knockdown of β-catenin or TCF7L2. In

addition, RNA-seq analysis coupled with genome-wide mapping of CEBPA- and FOXA1-binding regions reveals that these two factors also increase the expression of arginase 1 (ARG1) that

catalyzes the hydrolysis of arginine. Metabolome analysis discloses that activated Wnt signaling augments intracellular concentrations of histidine and arginine, and that the signal also

increases the level of lactic acid suggesting the induction of the Warburg effect in liver cancer cells. Further analysis reveals that the levels of metabolites of the urea cycle and genes

coding its related enzymes are also modulated by the Wnt signaling. These findings shed light on the altered cellular metabolism in the liver by the Wnt/β-catenin pathway through the

suppression of liver-enriched transcription factors including CEBPA and FOXA1. SIMILAR CONTENT BEING VIEWED BY OTHERS SLC13A3 IS A MAJOR EFFECTOR DOWNSTREAM OF ACTIVATED Β-CATENIN IN LIVER

CANCER PATHOGENESIS Article Open access 30 August 2024 ENERGY STRESS-INDUCED LINC01564 ACTIVATES THE SERINE SYNTHESIS PATHWAY AND FACILITATES HEPATOCELLULAR CARCINOGENESIS Article 19 March

2021 DEREGULATED 14-3-3Ζ AND METHIONINE ADENOSYLTRANSFERASE Α1 INTERPLAY PROMOTES LIVER CANCER TUMORIGENESIS IN MICE AND HUMANS Article 04 August 2021 INTRODUCTION Alterations in the genes

associated with the Wnt/β-catenin signaling pathway have been identified in various types of tumors1. In colorectal cancer, over 90% of the cases carry at least one mutation in genes

involved in this pathway such as inactivating mutations of the adenomatous polyposis coli (_APC_) gene (~80%) and activating mutations of the β-catenin (_CTNNB1_) gene (~5%)2. In

hepatocellular carcinoma (HCC), somatic mutations in _CTNNB1_ (31%) and _AXIN1_ (6%) have been frequently found3. These mutations result in the accumulation of β-catenin and its

translocation into the nucleus to form transcriptionally active complexes with T-cell factor/lymphoid enhancer factor (TCF/LEF) family proteins. Importantly, genes directly transactivated by

the complexes have been shown to play a key role in the development of tumors. For example, c-Myc and cyclin D1 regulate cell proliferation and/or cell cycle progression4,5. Although more

than one hundred target genes have been identified, most of the genes are transcriptionally up-regulated by the complex, and little attention have been paid to the down-regulated genes. We

previously reported that the suppressed expression of IRF1 by the signal plays a vital role in colorectal carcinogenesis, and its expression was regulated by the ubiquitin-proteasome pathway

through a deubiquitinase complex, USP1/UAF16. We also revealed that histidine ammonia-lyase (_HAL_), phosphoenolpyruvate carboxykinase 1 (_PCK1_), solute carrier family 51 subunit alpha

(_SLC51A_), pleckstrin 2 (_PLEK2_), integrin β3 (_ITGB3_), and secretory leukocyte protease inhibitor (_SLPI_) were transcriptionally down-regulated by the Wnt signaling pathway in liver

cancer7. Additionally, we identified a regulatory region of _HAL_, between −90 bp and −44 bp in the 5’-flanking region, and revealed that transcriptional activity of the region was

suppressed by the activation of Wnt/β-catenin signaling7. HAL is the rate-limiting enzyme of histidine catabolism and catalyzes l-histidine to urocanate and ammonia in the liver and skin.

Histidine metabolism is an important metabolic process involved in de novo synthesis of purine nucleic acids that accelerates the cancer cell progression due to the association with

production of tetrahydrofolate8. In addition, histamine, a metabolite converted from histidine, plays an important role in cancer immunity through the control of various responses in innate

and adaptive immunity9. Although the expression of HAL may play a crucial role in cancer cells, the regulatory mechanism remains to be clarified. Regarding the link between the canonical Wnt

signaling and metabolism, it was reported that the activation of the Wnt signaling pathway induced the Warburg effect through increased expression of _MYC_10, _PDK1_11, and lactate/pyruvate

transporter _MCT1/SLC11A1_12 or suppression of cytochrome oxidase13. Importantly, crosstalk between the Wnt and c-Myc pathways promotes glycolysis and energy production in cancer cells14.

In addition, glycolysis modulates Wnt signaling to promote axial elongation of the embryo in the tail bud15, suggesting a tight link between Wnt signaling and aerobic glycolysis. However,

little is known about the effect of Wnt signaling on other metabolisms. In this study, we clarified that _HAL_ was transcriptionally induced by CEBPA and FOXA1, and that their expression

levels were down-regulated by the Wnt/β-catenin signaling in the liver cancer cells. Furthermore, we found that activated Wnt/β-catenin signaling suppressed not only _HAL_ but also _ARG1_, a

gene encoding arginase 1, through the reduced CEBPA and FOXA1 expression. These data helped us gain insight into a role of the signal pathway in amino acid metabolism in liver cancer.

RESULTS IDENTIFICATION OF CANDIDATE TRANSCRIPTION FACTOR(S) REGULATING _HAL_ IN LIVER CANCER CELLS We previously uncovered that the 5’-flanking region of _HAL_, between −90 bp and −44 bp,

was responsible for the suppression of β-catenin/TCF complex in liver cancer cells7. Since the β-catenin/TCF complex induces downstream target genes, the decreased transcription of _HAL_ was

assumed to be indirectly regulated by the complex. In agreement with this notion, the region has no β-catenin/TCF7L2 ChIP-seq peaks or consensus TCF-binding motifs (ENCODE accession number:

ENCSR000EVQ). We searched for transcription factors (TFs) that are associated with the activity of the _HAL_ 5’-flanking region using JASPAR, a database of TF-binding profiles

(http://jaspar.genereg.net/). As a result, a total of 41 putative motifs including 33 TFs with a score of 8.0 or above were identified in the region (Supplementary Data 1a). We searched for

two types of TFs among the 33 identified, namely transcriptional repressors that were up-regulated by the Wnt/β-catenin pathway and activators that were down-regulated by the pathway.

Expression profile analysis was performed using HepG2 hepatoblastoma cells transfected with different siRNAs targeting β-catenin (−9 or −10), TCF7, TCF7L1, TCF7L2, LEF1, or their combination

(Supplementary Data 1b, c) and the expression levels of the 33 TFs were compared with those of downstream genes in the Wnt signaling pathway using the expression profile data (Supplementary

Fig. 1a). As expected, _MYC_, _RNF43_, _AXIN2_, and _LGR5_, four well-known Wnt target genes were down-regulated by the treatment with β-catenin, TCF7L2, or TCF7 siRNA (Fig. 1a). However,

TCF7L1 and LEF1 are unlikely to be involved in the activation of Wnt signaling in the cells, possibly due to the lower expression of _TCF7L1_ and _LEF1_ compared with other TCF family

members in HepG2 cells. We additionally confirmed that _HAL_ expression was remarkably enhanced by the treatment with β-catenin, TCF7L2, or TCF7 siRNA. A hierarchical clustering analysis

using expression values of the 33 TFs, _HAL_, and the four Wnt target genes identified subsets of genes that exhibited different expression patterns in response to the suppression of Wnt

signaling (Fig. 1a). Intriguingly, _FOXD1_ and _FOXP1_ were classified in a subgroup containing the four Wnt target genes (_MYC_, _RNF43_, _AXIN2_, and _LGR5_), suggesting that these two

genes may be up-regulated by the activation of Wnt signaling. FOXD1 is known to be a transcriptional activator16, but FOXP1 was reported to act primarily as a transcriptional repressor17,18.

Therefore, we included FOXP1 as a candidate suppressor of _HAL_. Additional qPCR analysis confirmed that _FOXP1_ expression was significantly decreased by the depletion of β-catenin- or

TCF7L2 in HepG2 cells (Supplementary Fig. 1b). Although we treated HuH-7 cells with two-independent FOXP1 siRNA, _HAL_ expression was not induced by the treatment (Supplementary Fig. 1c),

suggesting that FOXP1 may not suppress the transcription of _HAL_ in the cells. We additionally identified nine TFs including _CEBPA_, _FOXA1_, _FOXA3_, _FOXK1_, _FOXN3_, _FOXP3_, _NFATC2_,

_PROP1_, and _SRY_, which were classified in a subgroup containing _HAL_ by the cluster analysis, suggesting that these TFs are candidate transcriptional activator(s) regulating _HAL_. Among

the nine TFs, we focused on factors whose expression was correlated with _HAL_ in liver cancer tissues using the TCGA data (cBioportal, https://www.cbioportal.org/), because the negative

regulation of _HAL_ by Wnt signaling was most explicit in liver cancer, but not in colorectal cancer7. As a result, the expression levels of _FOXA3_ (_r_ = 0.36), _FOXA1_(_r_ = 0.25), and

_CEBPA_ (_r_ = 0.22) were significantly correlated with those of _HAL_ (_q_-value < 0.01, Supplementary Fig. 2a). To validate the association of their expression with the Wnt signaling,

we carried out qPCR and western blot analyses using HepG2 and HuH-6 cells carrying activating mutations in the _CTNNB1_ (β-catenin) gene. As expected, silencing of β-catenin or TCF7L2

increased the expression of CEBPA, FOXA1, and FOXA3 at the RNA and protein levels (Fig. 1b, c and Supplementary Fig. 2b). It is of note that these three TFs are known as liver-enriched

TFs19, and that _HAL_ and the three TFs are also abundantly expressed in normal liver tissue (Supplementary Fig. 2c, d). These data suggested that CEBPA, FOXA1, and FOXA3 were candidates

that transcriptionally regulate _HAL_ expression (Supplementary Fig. 2e). To investigate whether expression of the three factors was regulated in an Wnt/β-catenin-dependent manner in liver

cancer, we treated HuH-7 cells with a GSK-3α/β inhibitor, CHIR-99021, and examined their expression. As a result, the treatment significantly down-regulated the expression of CEBPA, FOXA1,

and FOXA3 (Fig. 1d). In complete agreement with these results, TCGA data showed that HCCs carrying mutant _CTNNB1_ have decreased expression of the three TFs as well as _HAL_ compared to

those with the wild-type (Fig. 1e). Taken together, these data strengthened our findings that CEBPA, FOXA1, and FOXA3 are down-regulated by the Wnt signaling. THE EFFECT OF CEBPA, FOXA1, AND

FOXA3 ON THE REPORTER ACTIVITY OF HAL Next, we examined the interaction of the three TFs with the regulatory region between −90 bp and −44 bp of _HAL_. The JASPAR database suggested two

CEBP-binding elements (CBE-1 and CBE-2) and a Forkhead-binding element (FBE) within the region to interact with both FOXA1 and FOXA3 (Supplementary Fig. 3a, b). To analyze whether these

three elements are functionally associated with the transcriptional activity, we used a reporter plasmid containing the _HAL_ promoter and its minimal regulatory region upstream of the

firefly luciferase gene (pHAL-90/+147)7. We generated four mutant reporter plasmids (Fig. 2a), namely a three-base substitution in CBE-1 (CBE-1m), a two-base substitution in CBE-2 (CBE-2m),

a three-base substitution in FBE (FBEm), and a combination of the three mutations (CBE-1m, CBE-2m, and FBEm). Reporter assay using the wild-type plasmids in combination with β-catenin siRNA

confirmed that knockdown of β-catenin increased the wild-type reporter activity in HepG2 cells (Fig. 2b). The increase in wild type reporter activity by the β-catenin siRNA was partially

reduced by the mutations of three binding motifs (CBE-1m, CBE-2m, and FBEm). Notably, the combination of the mutations in the three motifs almost abolished the increased reporter activity by

the β-catenin siRNA. We additionally analyzed the reporter activity of the wild-type plasmids by over-expression of the three TFs in HepG2 cells. As a result, over-expression of CEBPA alone

significantly augmented the reporter activity (Fig. 2c and Supplementary Fig. 4a). Furthermore, knockdown of CEBPA or FOXA1 with two-independent siRNAs for each gene significantly decreased

the reporter activity, but the activity was not changed by the knockdown of FOXA3 (Fig. 2d). CEBPA AND FOXA1 ARE BONA FIDE REGULATORS OF THE EXPRESSION OF HAL To further investigate the

involvement of these three TFs, we treated HuH-7 and Hep3B cells with CEBPA, FOXA1, or FOXA3 siRNAs and examined the expression of HAL by quantitative PCR and immunoblot analysis. Consistent

with the reporter assay results, knockdown of CEBPA or FOXA1 reduced HAL expression (Fig. 3a, b and Supplementary Fig. 4b), but that of FOXA3 did not show a consistent decrease in the

levels of HAL expression in both cell lines. In addition, HAL expression was induced by the overexpression of CEBPA or FOXA1 but not by that of FOXA3 (Fig. 3c, d and Supplementary Fig. 4c).

Taken together, our results suggest that CEBPA and FOXA1 play a vital role in the transcriptional activation of _HAL_ through the regulatory region located in the 5’-flanking region.

IDENTIFICATION OF DOWNSTREAM TARGETS OF CEBPA AND FOXA1 IN LIVER CANCER CELLS To unveil the roles of CEBPA and FOXA1 that were suppressed by the Wnt/β-catenin signaling pathway, we further

searched for additional target genes of these two TFs. RNA-seq analysis of HuH-7 cells treated with CEBPA or FOXA1 siRNAs identified genes which were down-regulated by the siRNAs. This

analysis corroborated that expression of _HAL_ was regulated by both CEBPA and FOXA1 (Supplementary Data 2a, b). We further performed ChIP-seq analysis and obtained significant peaks of the

interaction with CEBPA and FOXA1 across the genome (Supplementary Fig. 5a and Supplementary Data 2c, d). It is of note that CEBPA and FOXA1 peaks were observed in the 5’-flanking region of

the _HAL_ gene (Fig. 4a). An additional ChIP-qPCR analysis verified the interaction of this region with CEBPA and FOXA1 (Supplementary Fig. 5b). Integration of these data identified a total

of 460 and 489 potential direct target genes positively regulated by CEBPA and FOXA1, respectively (FDR _q_-values < 0.05, Fig. 4b and Supplementary Data 3a, b). To elucidate biological

roles of the two TFs, we performed enrichment analysis with the potential target genes using the KEGG pathway database. Consequently, 14 and 15 pathways were significantly related to CEBPA

and FOXA1 expression, respectively (FDR _q_-values < 0.01, Fig. 4c and Supplementary Data 3c, d). These included common pathways such as “Arginine and proline metabolism” and “Pathways in

cancer”. In addition, we searched for genes commonly down-regulated by knockdown of CEBPA and FOXA1, and identified a total of 132 genes including _HAL_ (Fig. 4d and Supplementary Data 3e).

Subsequent pathway analysis with the 132 genes uncovered significant enrichment of a gene set “Arginine and proline metabolism” (FDR _q_-value < 0.01, Fig. 4e and Supplementary Data 3f),

and this gene set included four differentially expressed genes, _AMD1_, _ARG1_, _GLS_, and _GOT1_ (Supplementary Fig. 6a). To investigate whether these four genes are down-regulated by the

Wnt signaling pathway, we analyzed their expression levels in HepG2 cells treated with control, β-catenin, or TCF7L2 siRNA. Among the four, only the expression of _ARG1_ was remarkably

increased by the suppression of β-catenin or TCF7L2 siRNA compared with control siRNA (Fig. 4f and Supplementary Fig. 6b). Consistent with our results, TCGA data showed that HCCs carrying

mutant _CTNNB1_ have decreased expression of _ARG1_ compared to those with the wild-type (Supplementary Fig. 6c). In addition, knockdown of CEBPA and FOXA1 decreased ARG1 expression

(Supplementary Fig. 6d, e). We also found significant CEBPA and FOXA1 peaks in intron 1 of the _ARG1_ gene in the ChIP-seq data (Fig. 4g and Supplementary Fig. 5c). To confirm whether the

Wnt/β-catenin signaling regulates _ARG1_ through the suppression of CEBPA and FOXA1, we analyzed their expression levels in HepG2 cells transfected with β-catenin with/without CEBPA or FOXA1

siRNA. As shown in Fig. 4h, knockdown of β-catenin alone increased the expression of CEBPA and FOXA1 as well as that of ARG1. Concomitant suppression of β-catenin and CEBPA or FOXA1

counteracted the induction of ARG1 by β-catenin siRNA. A similar result was obtained for HAL expression. These results suggest that Wnt signaling regulates the expression of HAL and ARG1

through CEBPA and FOXA1. WNT SIGNALING PATHWAY IS ASSOCIATED WITH THE LEVELS OF CELLULAR AMINO ACIDS The down-regulation of _HAL_ and _ARG1_ by Wnt signaling is likely dependent on the type

of tissue because the expression of _HAL_ and _ARG1_ did not decrease in the Wnt-activated colorectal adenocarcinoma (Supplementary Fig. 6f). Since these enzymes are involved in the

metabolism of histidine or arginine, we examined whether the levels of metabolites are regulated by the Wnt signaling pathway in liver cancer cells. Metabolome analysis was performed using

capillary electrophoresis time-of-flight mass spectrometry and the levels of 116 metabolites were compared in the lysates from HepG2 cells transfected with control, β-catenin, or TCF7L2

siRNA. The heatmap depicted that metabolites commonly altered by both β-catenin and TCF7L2 siRNAs were mainly reduced (Fig. 5a and Supplementary Data 4a). Consistent with the increased

expression of HAL and ARG1 by the siRNA, the levels of histidine and arginine were significantly decreased in the cells transfected with β-catenin or TCF7L2 siRNA (Fig. 5b). To determine

whether the levels of these amino acids were regulated by CEBPA or FOXA1, we analyzed the levels of histidine and arginine in HepG2 cells transfected with β-catenin siRNA in combination with

CEBPA or FOXA1 siRNA (Supplementary Fig. 7a). As expected, the levels of histidine and arginine were significantly restored by the additional knockdown of CEBPA. There was also a trend

toward restoration of histidine and arginine levels in cells treated with β-catenin and FOXA1 siRNAs. These results were consistent with the western blotting results showing that knockdown

of CEBPA and FOXA1 reduced the silencing effect of β-catenin on HAL and ARG1 expression (Fig. 4h). These findings further support the involvement of these transcription factors in cellular

amino acid metabolism. Interestingly, inhibition of the Wnt signaling pathway significantly decreased a large number of amino acids, resulting in the suppression of total levels of amino

acids (Supplementary Fig. 7b). In addition, we found that the suppression of the pathway decreased the levels of lactic acid and increased that of acetyl-CoA (Fig. 5c), implying that the

inhibition of the Wnt signaling pathway may suppress the Warburg effect in liver cancer cells. Subsequently, we performed pathway analysis using the data of 41 metabolites that were

significantly regulated by β-catenin and TCF7L2 siRNA, and identified the enrichment of 13 metabolite sets (FDR _q_-values < 0.01, Fig. 5d and Supplementary Data 4b). Consistent with the

change in the level of arginine, “Arginine and Proline Metabolism” was in the list of the 13 metabolite sets. It is of note that “Urea cycle” was listed as the most significant metabolite

set. This result suggests that the activated Wnt signaling pathway may regulate the urea cycle through down-regulation of _ARG1_ in hepatoma cells because _ARG1_ encodes the focal enzyme of

the urea cycle hydrolyzing l-arginine to urea and ʟ-ornithine. We further investigated whether other genes in the urea cycle were modulated by the Wnt signaling pathway using the microarray

data (Supplementary Data 1b). In addition to _ARG1_, ornithine transcarbamylase (_OTC_) expression was remarkably increased by the knockdown of β-catenin and TCF7L2 (Fig. 5e), and

argininosuccinate synthase 1 (_ASS1_) and argininosuccinate lyase (_ASL_) expression was also substantially increased by the knockdown. These results suggest that the metabolism of amino

acids and the urea cycle are altered by the activated Wnt/β-catenin signaling pathway and that these changes may contribute to the development and progression of liver cancer cells.

DISCUSSION In this study, we have shown that the promoter region of _HAL_ was transcriptionally regulated by transcription factors CEBPA and FOXA1, and that the two were down-regulated by

the Wnt signaling pathway in the liver cancer cells. In addition, we have uncovered that the pathway modulates intracellular metabolites at least in part by HAL and ARG1 through suppression

of CEBPA and FOXA1. It was previously reported that _HAL_ expression was significantly decreased in the liver of _Hnf4a_ (hepatocyte nuclear factor 4α)-knockout mice20. In addition,

over-expression of β-catenin decreased the expression of _HNF4A_ in Hep3B cells21. These data led us to additionally investigate whether HNF4A regulates _HAL_ in the same manner as CEBPA and

FOXA1. The knockdown of β-catenin and TCF7L2 increased the expression of _HNF4A_ (Supplementary Fig. 1d). Although knockdown of HNF4A significantly decreased the expression of _HAL_

(Supplementary Fig. 1e), it did not affect the reporter activity of the _HAL_ promoter (Supplementary Fig. 1f). Therefore, _HAL_ expression is regulated by at least three transcription

factors, CEBPA, FOXA1, and HNF4A in liver cells, and CEBPA and FOXA1 function through the interaction with the proximal promoter region of _HAL_. FOXA1 or hepatocyte nuclear factor 3α

(HNF3A), a liver-enriched transcription factor, was reported to decrease the transcription of _PIK3R1_, and suppress the viability and motility of HCC cells through the inhibition of

PI3K-Akt signaling22. In addition, FOXA1 positively regulates miR-122 that is specifically suppressed in liver tumors with poor prognosis23. These reports suggest that FOXA1 may function as

a tumor suppressor in HCC, and their results are consistent with our finding that the expression of FOXA1 was suppressed in liver cancer cells with activated Wnt signaling. In our reporter

gene assay, overexpression or suppression of FOXA1 had limited effect on the activity of the _HAL_ promoter (Fig. 2), but the expression levels of _HAL_ were dependent on FOXA1 expression

(Fig. 3). This discrepancy may be explained by the fact that FOXA1 is a pioneer factor, which recognizes specific DNA sequences exposed on the surface of a nucleosome and allows other

transcription factors and histone modifiers to access silent genes that are inaccessible to general transcription factors24,25. Our results indicated that alteration of chromatin structure

by FOXA1 might be essential for the regulation of the transcription of _HAL_. Alternatively, a regulatory region other than −90 and −44 bp may be involved in the regulation of _HAL_

transcription. Microarray expression analysis of HepG2 cells with β-catenin or TCF/LEF siRNA confirmed that known Wnt target genes including _MYC_, _RNF43_, _AXIN2_, and _LGR5_ were listed

as down-regulated genes (Fig. 1a). Several members of FOX-family transcription factors such as _FOXD1_ and _FOXP1_ were also similarly reduced by these siRNAs, suggesting that _FOXD1_ and

_FOXP1_ may be targets of the canonical Wnt signaling pathway. It is of note that FOXD1 is aberrantly overexpressed in colorectal cancer, and it promotes the progression of cancer through

the activation of the ERK1/2 signaling pathway 26. Regarding FOXP1, it has a paradoxical role in tumorigenesis, depending on the type of cancer 27. Both FOXD1 and FOXP1 have been reported as

activators of Wnt/β-catenin signaling in prostate cancer and diffuse large B cell lymphoma, respectively 28,29. Therefore, these factors may mediate a positive feedback loop in the Wnt

signaling pathway and accelerate the progression of Wnt-driven cancer. The role of these factors in liver carcinogenesis needs to be clarified in future studies. CEBPA is a transcription

factor that is abundantly expressed in the liver, skin, mammary gland, and adipose tissue (GTEx Portal; https://gtexportal.org/home/). The repression of CEBPA was shown in a wide range of

liver pathologies including liver fibrosis30 and cirrhosis31. It has been reported that the suppression of CEBPA was caused by the activation of Wnt signaling in mouse embryonic

fibroblasts32 and liver cancer cells33 at the protein levels. Reportedly, CEBPA protein was degraded by the interaction with tribbles homolog 2 (TRIB2) and constitutive photomorphogenesis 1

(COP1)34 or tripartite motif-containing protein 21 (TRIM21)35, and TRIB2 was induced by activation of Wnt/TCF signaling33. However, we showed in this study that _CEBPA_ was transcriptionally

reduced by the activation of Wnt signaling. Because the expression of _FOXA1_ and _CEBPA_ was not induced by the knockdown of β-catenin in colorectal cancer cells, transcription factors

that are expressed in liver tissues might be involved in the regulation of _CEBPA_ and/or _FOXA1_ expression. _HAL_ and _ARG1_, the two liver-specific Wnt target genes, encode enzymes

associated with amino acid metabolisms. HAL catalyzes the first reaction in histidine catabolism36. Reportedly, inhibition of the components of the histidine degradation pathway such as HAL

and AMDHD1 (amidohydrolase domain containing 1) induces the levels of tetrahydrofolate, which decreases the sensitivity of hematopoietic cancer cells to methotrexate8. In addition, the

sensitivity to methotrexate in lung cancer cells was decreased by the knockout of _HAL_8. These data suggest a link between chemoresistance of the cells and Wnt-activation. Therefore, the

Wnt/β-catenin pathway may be involved in chemoresistance and cell differentiation through the suppression of histidine catabolism in hepatoma cells. As for ARG1, which is abundantly

expressed in the liver, it cleaves arginine to urea and ornithine in the urea cycle37. In this study, we showed that suppressed Wnt signaling changed the levels of metabolites as well as

genes associated with the urea cycle (Fig. 5d, e). In agreement with our data, proteomics analysis of liver-specific _Apc_ knockout mice demonstrated that the most differentially expressed

proteins were related to a metabolic pathway, and that the expression of Arg1 and Cps1, urea cycle related enzymes, were suppressed in the knockout mice38. Therefore, activated Wnt signaling

may accumulate intracellular ammonia by suppression of the urea cycle in liver cancer. The suppression may help the synthesis of various proteins necessary to produce new daughter cells by

decreasing the degradation of amino acids39. Indeed, our results indicated that suppression of the signaling caused the reduction in the total amount of amino acids (Supplementary Fig. 7b),

which may be due to the acceleration of the urea cycle and degradation of amino acids (Fig. 5e). Importantly, it has been reported that the dysregulation of the urea cycle is widely observed

in liver tumors and profoundly affects carcinogenesis, mutagenesis, and immunotherapy response40. Targeting the urea cycle might be an attractive therapeutic strategy for patients with

liver tumor driven by the activated Wnt pathway. As shown in Fig. 5c, we observed reduced glycolysis by β-catenin or TCF7L2 siRNAs in HepG2 cells, suggesting that Wnt signaling is associated

with glycolysis. It has been reported that Wnt signaling up-regulates pyruvate dehydrogenase kinase-1 (PDK1) in colorectal cancer cells, leading to the enhanced glycolysis through the

inhibition of pyruvate dehydrogenase (PDH) activity11. Consistent with this report, we confirmed that the expression of _PDK1_ was also decreased by the suppression of Wnt signaling in HepG2

cells (Supplementary Data 1b), corroborating that PDK1 plays a pivotal role in glycolysis as a downstream of the Wnt signaling in liver tissue. In cancer cells, increased catabolism of

glucose (Warburg effect) anaerobically rather than aerobically leads increased glucose uptake and lactic acid production41. It is noteworthy that increased levels of lactic acid by enhanced

glycolysis promote immune evasion in tumors42,43. Therefore, the activation of the Wnt/β-catenin signaling pathway may, in part, suppress anti-tumor immunity by increased lactic acid

production44. In conclusion, we identified two transcription factors, CEBPA and FOXA1, enriched in the liver as cellular context-dependent targets of the Wnt signaling. Our findings will

provide insights into the relationship between liver metabolism and the Wnt signaling pathway. Further investigations of tissue-specific Wnt targets may uncover a role of the pathway

involved in human carcinogenesis. METHODS CELL CULTURE Human hepatoma cells, HepG2 and Hep3B, were obtained from the American Type Culture Collection (ATCC, Manassas, VA), and HuH-6 and

HuH-7 cells were obtained from the Japanese Collection of Research Bioresources Cell Bank (JCRB, Osaka, Japan). These cells were confirmed to be mycoplasma-free and authenticated by Hoechst

DNA staining and short tandem repeat profiling, respectively (ATCC and JCRB). Retrovirus packaging cells PLAT-A were provided from Dr. Toshio Kitamura (The University of Tokyo). All cells

were grown in appropriate media containing 10% fetal bovine serum (Biosera, Nuaille, France) and 1% penicillin/streptomycin solution (Fujifilm Wako Pure Chemical, Osaka, Japan). The PLAT-A

cells were maintained in complete medium supplemented with puromycin (1 μg/ml, Merck, Darmstadt, Germany) and blasticidin (10 μg/ml, Fujifilm Wako Pure Chemical). EXPRESSION PLASMIDS The

entire coding region of _FOXA1_, _FOXA3_, and _HNF4A_ were amplified by RT-PCR (KOD One, Toyobo, Osaka, Japan) using human liver cDNA as template. The coding region of _CEBPA_ was amplified

using MSCV-CEBPA plasmid (a kind gift from Dr. Atsushi Iwama, The University of Tokyo). The PCR products were cloned into pCMV-Myc-N vector (Takara Bio, Shiga, Japan). The primer sequences

used for the amplification are listed in Supplementary Data 5a. The cloned DNA fragments in the plasmids were confirmed by Sanger sequencing (3500xL GeneticAnalyzer, Thermo Fisher

Scientific, Waltham, MA). GENE SILENCING Two independent siRNAs targeting each gene were used for gene silencing. CEBPA, FOXA1, FOXA3, and HNF4A siRNAs were purchased from Integrated DNA

Technologies (Coralville, IA). β-catenin, LEF1, TCF7, TCF7L1, and TCF7L2 siRNAs were obtained from Merck. Control siRNA (ON-TARGETplus Non-targeting Pool) was purchased from Horizon

Discovery (Cambridge, UK). The target sequences of the siRNAs are shown in Supplementary Data 5b. Cells were transfected with the siRNAs (10 nM) for 48 h using Lipofectamine RNAiMAX (Thermo

Fisher Scientific). The gene-silencing efficiency of each siRNA was confirmed by quantitative RT-PCR (qRT-PCR) or immunoblotting. QUANTITATIVE PCR Total RNA was isolated from cultured cells

using RNeasy Mini Kit (Qiagen, Valencia, CA). cDNA was synthesized from 1 μg of total RNA using ReverTraAce (Toyobo). qPCR was performed using KAPA SYBR Fast qPCR Kit (Kapa Biosystems,

Wilmington, MA) and StepOnePlus (Thermo Fisher Scientific) with sets of primers listed in Supplementary Data 5a. The levels of transcripts were determined using the relative standard curve

method, and hypoxanthine phosphoribosyl transferase 1 (_HPRT1_) was used as an internal control. IMMUNOBLOTTING Cells were lysed in radioimmunoprecipitation assay buffer (50 mM Tris-HCl, pH

8.0, 150 mM NaCl, 0.5% sodium deoxycholate, 1% Nonidet P-40, 0.1% sodium dodecyl sulfate) supplemented with a Protease Inhibitor Cocktail Set III (Merck). The proteins were separated by

SDS-PAGE and transferred to a nitrocellulose membrane (Cytiva, Marlborough, MA). After blocking with 5% skim milk powder in TBS-T (Tris-buffered saline-Tween20), the membranes were incubated

with anti-HAL (Merck, Cat# HPA038547, 1:1000), anti-β-catenin (Cell Signaling Technology, Danvers, MA, Cat# 9582, 1:1000), anti-TCF7L2 (Merck, Cat#05-511, 1:1000), anti-AXIN2 (Cell

Signaling Technology, Cat# 2151, 1:1000), anti-CEBPA (Thermo Fisher Scientific, Cat# PA5-77911, 1:1000), anti-FOXA1 (Merck, Cat# 17-10267, 1:1000 and Santa Cruz Biotechnology, Santa Cruz,

CA, Cat# sc-101058, 1:1000), anti- FOXA3 (Abcam, Cambridge, UK, Cat# ab108454, 1:1000), anti-ARG1 (Santa Cruz Biotechnology, Cat# sc-365547, 1:1000), anti-GFP (Santa Cruz Biotechnology, Cat#

sc-9996, 1:1000), or anti-β-actin (Merck, Cat# A5441, 1:2500) antibody overnight at 4 °C. Horseradish peroxidase-conjugated anti-mouse (NA931V, Cytiva), anti-rabbit IgG (NA9340V, Cytiva),

or VeriBlot for IP Detection Reagent (HRP) (ab131366, Abcam) served as the secondary antibody. The membranes were incubated with ImmunoStar LD (Fujifilm Wako Pure Chemical) or SuperSignal

West Pico Chemiluminescent Substrate (Thermo Fisher Scientific), and the chemiluminescence images were captured using Amersham Imager 600 system (Cytiva) or ChemiDoc XRS+ System (Bio-rad,

Hercules, CA). β-actin was used as a loading control. Probing with a loading control was performed in parallel with the target antibody by cutting the membrane prior to antibody incubation.

SITE-DIRECTED MUTAGENESIS Putative FOX and/or CEBP transcription factor binding motifs in the 5’-flanking region of the _HAL_ gene (pHAL −90/+147) were mutated by site-directed mutagenesis7.

The 5’-flanking region of _HAL_, between −90 bp and +147 bp, was amplified using KOD-Plus-Neo (Toyobo) with each primer set (Supplementary Data 5a). The PCR products were digested with

_Dpn_ I (Takara Bio) for 2 h at 37 °C, followed by transformation into _E. coli_. Successful mutagenesis was confirmed by Sanger sequencing. TREATMENT WITH GSK-3Α/Β INHIBITOR A GSK-3α/β

inhibitor, CHIR-99021, was purchased from MedChemExpress (Monmouth Junction, NJ), and dissolved in dimethyl sulfoxide (DMSO). HuH-7 cells were treated with CHIR-99021 (5 µM) or DMSO for 24 

h. LUCIFERASE REPORTER ASSAYS HepG2 and Hep3B cells seeded on 12-well plates were transfected with 0.25 µg of reporter plasmids and 0.05 µg of pRL-null (Promega, Madison, WI) in combination

with the indicated siRNAs using Lipofectamine 2000 reagent (Thermo Fisher Scientific). After 48 h, the cells were harvested, and reporter activity was measured by dual luciferase assay

system (TOYO B-Net, Tokyo, Japan). To examine the effect of FOXA1, FOXA3, and CEBPA on the activity of _HAL_ promoter, HepG2 cells were co-transfected with HAL reporter plasmid (0.2 µg) and

pRL-null plasmid (0.05 µg) in combination with plasmids expressing FOXA1 (0.2 µg), FOXA3 (0.2 µg), or CEBPA (0.02 µg) using FuGENE6 reagent (Promega). Firefly luciferase activity was

normalized to _Renilla_ luciferase activity (pRL-null). RETROVIRAL TRANSDUCTION _CEBPA_, _FOXA1_, and _FOXA3_ were sub-cloned into pMXs-Puro retroviral vector (a kind gift from Dr Kitamura,

The University of Tokyo) by the Gibson Assembly cloning method (NEBuilder HiFi DNA Assembly Master Mix, New England Biolabs, Ipswich, MA). The sequences of primers used for the amplification

are shown in Supplementary Data 5a. For production of retroviral particles, PLAT-A packaging cells were transfected with pMXs-EGFP (enhanced green fluorescent protein), pMXs-CEBPA,

pMXs-FOXA1, or pMXs-FOXA3 for 24 h. After changing the medium, the cells were further incubated for 24 h, and supernatants containing the retrovirus were collected and used for infection.

Forty-eight hours after infection, HepG2 cells were selected in the medium containing 1.625 µg/ml of puromycin, and Hep3B and HuH-7 cells were selected with 2.5 µg/ml of puromycin.

MICROARRAY ANALYSIS Total RNA was isolated from HepG2 cells treated with β-catenin, TCF7, TCF7L1, TCF7L2, or LEF1 siRNA (10 nM) for 48 h using Lipofectamine RNAiMAX. Total RNA was extracted

using the RNeasy Plus Mini kit (Qiagen), and subsequently expression profiles were analyzed by SurePrint G3 Gene Expression 8 × 60K microarray (Agilent Technologies, Santa Clara, CA)

according to the manufacturer’s protocol. Data processing and unsupervised hierarchical clustering were performed using GeneSpring GX14.1 software (Agilent Technologies). In hierarchical

clustering, Pearson’s center and centroid linkage were used as distance metric and linkage function, respectively. RNA-SEQ ANALYSIS HuH-7 cells were transfected with control, FOXA1, or CEBPA

siRNAs (10 nM) for 48 h. Total RNA was extracted from the cells using the RNeasy Plus Mini kit. Agilent Bioanalyzer device (Agilent Technologies) was used to assess the quality of extracted

RNA. Subsequently, RNA-seq libraries were prepared with total RNA (100 ng) using an NEBNext Ultra II Directional RNA Library Prep Kit (New England Biolabs) according to the manufacturer’s

protocol. The libraries were sequenced with 100 bp paired-end reads on the DNBSEQ-G400RS (MGI Tech, Shenzhen, China). The analysis of sequencing data was performed by a standard RNA-seq

analytical pipeline. Briefly, STAR(v2.7.3a)45 was used to align the sequencing data to the human genome (hg38). Quantification of gene expression was performed using RSEM (v1.3.3)46. The

DESeq2 package (v1.26.0)47 was used to normalize the read count data and test for differential gene expression. CHROMATIN IMMUNOPRECIPITATION FOLLOWED BY HIGH-THROUGHPUT SEQUENCING

(CHIP-SEQ) ChIP-seq was performed using anti-CEBPA (Thermo Fisher Scientific, Cat# PA5-77911) or anti-FOXA1 antibody (Merck, Cat# 17-10267)48. HuH-7 cells were cross-linked with 1%

formaldehyde for 10 min at room temperature, and 0.1 M glycine was added to quench the formaldehyde. Chromatin extracts were sheared by micrococcal nuclease digestion (New England Biolabs),

and protein-DNA complexes were immunoprecipitated with 5 μg of anti-CEBPA or anti-FOXA1 antibody bound to Dynabeads Protein G (Thermo Fisher Scientific). Normal rabbit IgG (Santa Cruz

Biotechnology) was used as a negative control. De-crosslinking was performed at 65 °C overnight, and samples were subsequently treated with RNase A (Merck) for 2 h at 37 °C and Proteinase K

(Merck) for 30 min at 55 °C. The precipitated protein-DNA complexes were purified by the conventional DNA extraction method, and the purified DNA was used for preparation of sequence

libraries. Concentration of input and ChIP’d DNA was measured using Qubit dsDNA HS Assay kit (Thermo Fisher Scientific). ChIP-seq libraries were prepared with 1 ng of DNA using an NEBNext

Ultra II DNA Library Prep kit (New England Biolabs) according to the manufacturer’s protocol. The libraries were sequenced with 150 bp paired-end reads on the DNBSEQ-G400RS. The sequencing

data were aligned to human genome (GRCh38) using Bowtie2 (v2.4.1)49. Peak calling followed by assignation of peaks to genes was performed using MACS2 (v3.6)50 and HOMER (v4.11)51. Peaks with

_q_-value < 0.05 were considered significant. To validate the results of ChIP-seq analysis, ChIP followed by quantitative PCR (ChIP-qPCR) was performed using KAPA SYBR Fast qPCR Kit

(Kapa Biosystems) and StepOnePlus (Thermo Fisher Scientific)52. The precipitated DNAs were subjected to qPCR analysis using sets of primers encompassing genomic regions with peaks. As

negative control non-immune IgG and control primers to amplify exon 1 of the glyceraldehyde-3-phosphate dehydrogenase (_GAPDH_) were used. The primer sequences used are listed in

Supplementary Data 5a. METABOLITE EXTRACTION AND ANALYSIS After aspiration of culture medium, the cells were washed twice with 5% mannitol solution, and incubated with methanol at room

temperature for 30 s to suppress enzyme activity. Next, internal standards (H3304-1002, Human Metabolome Technologies, Yamagata, Japan) were added to the cell extract, followed by further

incubation at room temperature for 30 s. The cell extract was then centrifuged at 2300 × _g_, 4 °C for 5 min, after which the supernatant was centrifugally filtered through a Millipore 5-kDa

cutoff filter (UltrafreeMC-PLHCC, Human Metabolome Technologies) at 9100 × _g_, 4 °C for 5 h to remove macromolecules. Subsequently, the filtrate was evaporated to dryness under vacuum and

reconstituted in Milli-Q water for metabolome analysis at Human Metabolome Technologies. Metabolome analysis was conducted according to the C-SCOPE package (Human Metabolome Technologies),

using capillary electrophoresis time-of-flight mass spectrometry (CE-TOFMS) for cation analysis and CE-tandem mass spectrometry (CE-MS/MS) for anion analysis based on the methods described

previously 53,54. Peaks were extracted using MasterHands, automatic integration software (Keio University, Yamagata, Japan)55 and MassHunter Quantitative Analysis B.04.00 (Agilent

Technologies) in order to obtain peak information including m/z, peak area, and migration time (MT). Signal peaks were annotated according to the metabolite database of Human Metabolome

Technologies, based on their m/z values and MTs. The peak area of each metabolite was normalized to internal standards, and metabolite concentration was evaluated by standard curves with

three-point calibrations using each standard compound. Hierarchical cluster analysis56 was performed by Human Metabolome Technologies’s proprietary MATLAB and R programs, respectively.

Detected metabolites were plotted on metabolic pathway maps using VANTED software57. OVER-REPRESENTATION ANALYSIS (ORA) The biological significance of the expression and metabolome data was

assessed by over-representation analysis. Differentially expressed genes by either CEBPA or FOXA1 siRNAs were subjected to KEGG pathway analysis. Gene sets with FDR _q_-value < 0.01 were

considered significant. MetaboAnalyst58 was used for the analysis of differential levels of metabolites by β-catenin and TCF7L2 siRNA. Metabolite sets with FDR _q_-value < 0.01 were

considered significant. STATISTICS AND REPRODUCIBILITY The unpaired two-tailed t-test was used when two independent groups were compared. For groups larger than two, statistical analysis was

performed using one-way analysis of variance (ANOVA) with Dunnett’s post hoc test. These statistical analyses were performed using the BellCurve for Excel software (Social Survey Research

Information, Tokyo, Japan). A _p_-value < 0.05 was considered statistically significant. Sample sizes are indicated in the figure legends. Data are displayed with error bars showing mean 

± SD and individual samples in a bar graph. In RNA-seq and ChIP-seq analyses, significance level was set at a Benjamini–Hochberg FDR-adjusted _p_-value (i.e., _q_-value) of less than 0.05.

REPORTING SUMMARY Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article. DATA AVAILABILITY Microarray (GSE244527), RNA-seq

(GSE244526), and ChIP-seq (GSE244525) data generated in this study were deposited in the Gene Expression Omnibus (GEO) database. Plasmids generated in this study were deposited into Addgene

(pCMV-Myc-CEBPA: 219393, pCMV-Myc-FOXA1: 219394, pCMV-Myc-FOXA3: 219395, pCMV-Myc-HNF4A: 21939). All data supporting the findings of this study are available within the paper, its

Supplementary Figs., and Supplementary Data. The source data for the graphs in this study are provided in Supplementary Data 5c–g. All uncropped blots are provided in Supplementary Figs. 

8–16. REFERENCES * Sanchez-Vega, F. et al. Oncogenic signaling pathways in the Cancer Genome Atlas. _Cell_ 173, 321–337.e10 (2018). Article  CAS  PubMed  PubMed Central  Google Scholar  *

Muzny, D. M. et al. Comprehensive molecular characterization of human colon and rectal cancer. _Nature_ 487, 330–337 (2012). Article  CAS  Google Scholar  * Totoki, Y. et al. Trans-ancestry

mutational landscape of hepatocellular carcinoma genomes. _Nat. Genetics_ 46, 1267–1273 (2014). Article  CAS  PubMed  Google Scholar  * Sansom, O. J. et al. Myc deletion rescues Apc

deficiency in the small intestine. _Nature_ 446, 676–679 (2007). Article  CAS  PubMed  Google Scholar  * Tetsu, O. & McCormick, F. Beta-catenin regulates expression of cyclin D1 in colon

carcinoma cells. _Nature_ 398, 422–426 (1999). Article  CAS  PubMed  Google Scholar  * Ohsugi, T. et al. Anti-apoptotic effect by the suppression of IRF1 as a downstream of Wnt/β-catenin

signaling in colorectal cancer cells. _Oncogene_ 38, 6051–6064 (2019). Article  CAS  PubMed  Google Scholar  * Yamaguchi, K. et al. Bidirectional reporter assay using HAL promoter and

TOPFLASH improves specificity in high-throughput screening of Wnt inhibitors. _Biotechnol. Bioeng._ 114, 2868–2882 (2017). Article  CAS  PubMed  Google Scholar  * Kanarek, N. et al.

Histidine catabolism is a major determinant of methotrexate sensitivity. _Nature_ 559, 632–636 (2018). Article  CAS  PubMed  PubMed Central  Google Scholar  * De La, M., Sarasola, P.,

Delgado, M. A. T. & Nicoud, M. B. Histamine in cancer immunology and immunotherapy. Current status and new perspectives. _Pharmacol. Res. Perspect._ 9, 27 (2021). Google Scholar  * Dang,

C. V., Le, A. & Gao, P. MYC-induced cancer cell energy metabolism and therapeutic opportunities. _Clin. Cancer Res._ 15, 6479–6483 (2009). Article  CAS  PubMed  PubMed Central  Google

Scholar  * Pate, K. T. et al. Wnt signaling directs a metabolic program of glycolysis and angiogenesis in colon cancer. _EMBO J._ 33, 1454–1473 (2014). Article  CAS  PubMed  PubMed Central 

Google Scholar  * Sprowl-Tanio, S. et al. Lactate/pyruvate transporter MCT-1 is a direct Wnt target that confers sensitivity to 3-bromopyruvate in colon cancer. _Cancer Metab._ 4, 20 (2016).

Article  PubMed  PubMed Central  Google Scholar  * Lee, S. Y. et al. Wnt/snail signaling regulates cytochrome c oxidase and glucose metabolism. _Cancer Res._ 72, 3607–3617 (2012). Article 

CAS  PubMed  Google Scholar  * Sherwood, V. WNT signaling: an emerging mediator of cancer cell metabolism? _Mol. Cell Biol._ 35, 2–10 (2015). Article  PubMed  Google Scholar  * Oginuma, M.

et al. A gradient of glycolytic activity coordinates FGF and Wnt signaling during elongation of the body axis in amniote embryos. _Dev. Cell_ 40, 342–353.e10 (2017). Article  CAS  PubMed 

PubMed Central  Google Scholar  * Laissue, P. et al. Association of FOXD1 variants with adverse pregnancy outcomes in mice and humans. _Open Biol._ 6, 160109 (2016). Article  PubMed  PubMed

Central  Google Scholar  * Shu, W., Yang, H., Zhang, L., Lu, M. M. & Morrisey, E. E. Characterization of a new subfamily of winged-helix/forkhead (Fox) genes that are expressed in the

lung and act as transcriptional repressors. _J. Biol. Chem._ 276, 27488–27497 (2001). Article  CAS  PubMed  Google Scholar  * Li, S., Weidenfeld, J. & Morrisey, E. E. Transcriptional and

DNA binding activity of the Foxp1/2/4 family is modulated by heterotypic and homotypic protein interactions. _Mol. Cell Biol._ 24, 809–822 (2004). Article  CAS  PubMed  PubMed Central 

Google Scholar  * Schrem, H., Jü, J., Klempnauer, J. & Borlak, J. Liver-enriched transcription factors in liver function and development. Part I: the hepatocyte nuclear factor network

and liver-specific gene expression. _Pharmacol. Rev._ 54, 129–158 (2002). Article  CAS  PubMed  Google Scholar  * Gougelet, A. et al. T-cell factor 4 and β-catenin chromatin occupancies

pattern zonal liver metabolism in mice. _Hepatology_ 59, 2344–2357 (2014). Article  CAS  PubMed  Google Scholar  * Yang, M. et al. A double-negative feedback loop between Wnt-β-catenin

signaling and HNF4α regulates epithelial-mesenchymal transition in hepatocellular carcinoma. _J. Cell. Sci._ 126, 5692–5703 (2013). CAS  PubMed  Google Scholar  * He, S., Zhang, J., Zhang,

W., Chen, F. & Luo, R. FOXA1 inhibits hepatocellular carcinoma progression by suppressing PIK3R1 expression in male patients. _J. Exp. Clin. Cancer Res._ 36, 175 (2017). Article  PubMed

  PubMed Central  Google Scholar  * Coulouarn, C., Factor, V. M., Andersen, J. B., Durkin, M. E. & Thorgeirsson, S. S. Loss of miR-122 expression in liver cancer correlates with

suppression of the hepatic phenotype and gain of metastatic properties. _Oncogene_ 28, 3526 (2009). Article  CAS  PubMed  PubMed Central  Google Scholar  * Cirillo, L. A. et al. Opening of

compacted chromatin by early developmental transcription factors HNF3 (FoxA) and GATA-4. _Mol. Cell_ 9, 279–289 (2002). Article  CAS  PubMed  Google Scholar  * Balsalobre, A. & Drouin,

J. Pioneer factors as master regulators of the epigenome and cell fate. _Nat. Rev. Mol. Cell Biol._ 23, 449–464 (2022). Article  CAS  PubMed  Google Scholar  * Pan, F., Li, M. & Chen, W.

Original article FOXD1 predicts prognosis of colorectal cancer patients and promotes colorectal cancer progression via the ERK 1/2 pathway. _Am. J. Transl. Res._ 10, 1522–1530 (2018). CAS 

PubMed  PubMed Central  Google Scholar  * Koon, H. B., Ippolito, G. C., Banham, A. H. & Tucker, P. W. FOXP1: a potential therapeutic target in cancer. _Expert Opin. Ther. Targets_ 11,

955–965 (2007). Article  CAS  PubMed  PubMed Central  Google Scholar  * Donmez, C. & Konac, E. Silencing effects of FOXD1 inhibit metastatic potentials of the PCa via N-cadherin –

Wnt/β-catenin crosstalk. _Gene_ 836, 146680 (2022). Article  CAS  PubMed  Google Scholar  * Walker, M. P. et al. FOXP1 potentiates Wnt/β-catenin signaling in diffuse large B cell lymphoma.

_Sci. Signal._ 8, ra12 (2015). Article  PubMed  PubMed Central  Google Scholar  * Mei, S., Wang, X., Zhang, J., Qian, J. & Ji, J. In vivo transfection of C/EBP-α gene could ameliorate

CCL4-induced hepatic fibrosis in mice. _Hepatol. Res._ 37, 531–539 (2007). Article  CAS  PubMed  Google Scholar  * Tao, L. L. et al. C/EBP-α ameliorates CCl 4-induced liver fibrosis in mice

through promoting apoptosis of hepatic stellate cells with little apoptotic effect on hepatocytes in vitro and in vivo. _Apoptosis_ 17, 492–502 (2012). Article  CAS  PubMed  Google Scholar 

* Ross, S. E. et al. Inhibition of adipogenesis by Wnt signaling. _Science_ 289, 950–953 (2000). _(1979)_. Article  CAS  PubMed  Google Scholar  * Wang, J. et al. TRIB2 acts downstream of

Wnt/TCF in liver cancer cells to regulate YAP and C/EBPα function. _Mol. Cell_ 51, 211 (2013). Article  CAS  PubMed  PubMed Central  Google Scholar  * Keeshan, K. et al. Transformation by

Tribbles homolog 2 (Trib2) requires both the Trib2 kinase domain and COP1 binding. _Blood_ 116, 4948–4957 (2010). Article  CAS  PubMed  PubMed Central  Google Scholar  * Grandinetti, K. B.

et al. Overexpression of TRIB2 in human lung cancers contributes to tumorigenesis through downregulation of C/EBPα. _Oncogene_ 30, 3328–3335 (2011). Article  CAS  PubMed  PubMed Central 

Google Scholar  * PETERKOFSKY, A. The mechanism of action of histidase: amino-enzyme formation and partial reactions. _J. Biol. Chem._ 237, 787–795 (1962). Article  CAS  PubMed  Google

Scholar  * Sin, Y. Y., Baron, G., Schulze, A. & Funk, C. D. Arginase-1 deficiency. _J. Mol. Med._ 93, 1287–1296 (2015). Article  CAS  PubMed  Google Scholar  * Chafey, P. et al.

Proteomic analysis of β-catenin activation in mouse liver by DIGE analysis identifies glucose metabolism as a new target of the Wnt pathway. _Proteomics_ 9, 3889–3900 (2009). Article  CAS 

PubMed  Google Scholar  * Chandel, N. S. Amino acid metabolism. _Cold Spring Harb. Perspect. Biol._ 13, a040584 (2021). Article  CAS  PubMed  PubMed Central  Google Scholar  * Lee, J. S. et

al. Urea cycle dysregulation generates clinically relevant genomic and biochemical signatures. _Cell_ 174, 1559–1570.e22 (2018). Article  CAS  PubMed  PubMed Central  Google Scholar  *

Liberti, M. V. & Locasale, J. W. The Warburg effect: how does it benefit cancer cells? _Trends Biochem. Sci._ 41, 211–218 (2016). Article  CAS  PubMed  PubMed Central  Google Scholar  *

Fischer, K. et al. Inhibitory effect of tumor cell–derived lactic acid on human T cells. _Blood_ 109, 3812–3819 (2007). Article  CAS  PubMed  Google Scholar  * Wang, Z. H., Peng, W. B.,

Zhang, P., Yang, X. P. & Zhou, Q. Lactate in the tumour microenvironment: from immune modulation to therapy. _EBioMedicine_ 73, 103627 (2021). Article  CAS  PubMed  PubMed Central 

Google Scholar  * Wang, B., Tian, T., Kalland, K. H., Ke, X. & Qu, Y. Targeting Wnt/β-catenin signaling for cancer immunotherapy. _Trends Pharmacol. Sci._ 39, 648–658 (2018). Article 

CAS  PubMed  Google Scholar  * Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. _Bioinformatics_ 29, 15–21 (2013). Article  CAS  PubMed  Google Scholar  * Li, B. & Dewey, C.

N. RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. _BMC Bioinform._ 12, 323 (2011). Article  CAS  Google Scholar  * Love, M. I., Huber, W.

& Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. _Genome Biol._ 15, 1–21 (2014). Article  Google Scholar  * Yamaguchi, K. et al. Bromodomain

protein BRD8 regulates cell cycle progression in colorectal cancer cells through a TIP60-independent regulation of the pre-RC complex. _iScience_ 26, 106563 (2023). Article  CAS  PubMed 

PubMed Central  Google Scholar  * Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. _Nat. Methods_ 9, 357–359 (2012). Article  CAS  PubMed  PubMed Central  Google

Scholar  * Zhang, Y. et al. Model-based analysis of ChIP-Seq (MACS). _Genome Biol._ 9, R137 (2008). Article  PubMed  PubMed Central  Google Scholar  * Heinz, S. et al. Simple combinations of

lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities. _Mol. Cell_ 38, 576–589 (2010). Article  CAS  PubMed  PubMed Central 

Google Scholar  * Yamaguchi, K. et al. Overexpression of cohesion establishment factor DSCC1 through E2F in colorectal cancer. _PLoS ONNE_ 9, e85750 (2014). Article  Google Scholar  *

Ohashi, Y. et al. Depiction of metabolome changes in histidine-starved _Escherichia coli_ by CE-TOFMS. _Mol. Biosyst._ 4, 135–147 (2008). Article  CAS  PubMed  Google Scholar  * Ooga, T. et

al. Metabolomic anatomy of an animal model revealing homeostatic imbalances in dyslipidaemia. _Mol. Biosyst._ 7, 1217–1223 (2011). Article  CAS  PubMed  Google Scholar  * Sugimoto, M., Wong,

D. T., Hirayama, A., Soga, T. & Tomita, M. Capillary electrophoresis mass spectrometry-based saliva metabolomics identified oral, breast and pancreatic cancer-specific profiles.

_Metabolomics_ 6, 78–95 (2010). Article  CAS  PubMed  Google Scholar  * Yamamoto, H. et al. Statistical hypothesis testing of factor loading in principal component analysis and its

application to metabolite set enrichment analysis. _BMC Bioinform._ 15, 51 (2014). Article  Google Scholar  * Junker, B. H., Klukas, C. & Schreiber, F. Vanted: a system for advanced data

analysis and visualization in the context of biological networks. _BMC Bioinform._ 7, 109 (2006). Article  Google Scholar  * Pang, Z. et al. Using MetaboAnalyst 5.0 for LC–HRMS spectra

processing, multi-omics integration and covariate adjustment of global metabolomics data. _Nat. Protoc._ 17, 1735–1761 (2022). Article  CAS  PubMed  Google Scholar  Download references

ACKNOWLEDGEMENTS We thank Yumiko Isobe, Seira Hatakeyama, and Yuqing Huang (The University of Tokyo) for their technical assistance. The super-computing resource was provided by Human Genome

Center, The Institute of Medical Science, The University of Tokyo (http://sc.hgc.jp/shirokane.html). This work was supported by JSPS KAKENHI grant number JP20K07563 to K.Y. and Takeda

Science Foundation to K.Y. AUTHOR INFORMATION AUTHORS AND AFFILIATIONS * Division of Clinical Genome Research, Advanced Clinical Research Center, The Institute of Medical Science, The

University of Tokyo, Tokyo, 108-8639, Japan Saya Nakagawa, Kiyoshi Yamaguchi, Kiyoko Takane, Tsuneo Ikenoue & Yoichi Furukawa * Tsuruoka Metabolomics Laboratory, National Cancer Center,

Tsuruoka, Yamagata, 997-0052, Japan Sho Tabata Authors * Saya Nakagawa View author publications You can also search for this author inPubMed Google Scholar * Kiyoshi Yamaguchi View author

publications You can also search for this author inPubMed Google Scholar * Kiyoko Takane View author publications You can also search for this author inPubMed Google Scholar * Sho Tabata

View author publications You can also search for this author inPubMed Google Scholar * Tsuneo Ikenoue View author publications You can also search for this author inPubMed Google Scholar *

Yoichi Furukawa View author publications You can also search for this author inPubMed Google Scholar CONTRIBUTIONS Conceptualization: K.Y., S.N., Y.F. Formal Analysis, S.N., K.Y.

Investigation: S.N., K.Y. Methodology: S.N., K.Y., K.T., S.T., T.I., Y.F. Visualization: S.N., K.Y. Writing—Original Draft: S.N., K.Y., Y.F. Writing—Review & Editing: S.N., K.Y., K.T.,

S.T., T.I., Y.F. Funding Acquisition: K.Y. Supervision: K.Y., Y.F. CORRESPONDING AUTHORS Correspondence to Kiyoshi Yamaguchi or Yoichi Furukawa. ETHICS DECLARATIONS COMPETING INTERESTS The

authors declare no competing interests. PEER REVIEW PEER REVIEW INFORMATION _Communications Biology_ thanks Serif Senturk and the other, anonymous, reviewer(s) for their contribution to the

peer review of this work. Primary Handling Editors: Joao Valente. A peer review file is available. ADDITIONAL INFORMATION PUBLISHER’S NOTE Springer Nature remains neutral with regard to

jurisdictional claims in published maps and institutional affiliations. SUPPLEMENTARY INFORMATION PEER REVIEW FILE SUPPLEMENTARY INFORMATION DESCRIPTION OF ADDITIONAL SUPPLEMENTARY FILES

SUPPLEMENTARY DATA 1 SUPPLEMENTARY DATA 2 SUPPLEMENTARY DATA 3 SUPPLEMENTARY DATA 4 SUPPLEMENTARY DATA 5 REPORTING SUMMARY RIGHTS AND PERMISSIONS OPEN ACCESS This article is licensed under a

Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit

to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are

included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and

your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this

licence, visit http://creativecommons.org/licenses/by/4.0/. Reprints and permissions ABOUT THIS ARTICLE CITE THIS ARTICLE Nakagawa, S., Yamaguchi, K., Takane, K. _et al._ Wnt/β-catenin

signaling regulates amino acid metabolism through the suppression of CEBPA and FOXA1 in liver cancer cells. _Commun Biol_ 7, 510 (2024). https://doi.org/10.1038/s42003-024-06202-9 Download

citation * Received: 04 November 2023 * Accepted: 16 April 2024 * Published: 29 April 2024 * DOI: https://doi.org/10.1038/s42003-024-06202-9 SHARE THIS ARTICLE Anyone you share the following

link with will be able to read this content: Get shareable link Sorry, a shareable link is not currently available for this article. Copy to clipboard Provided by the Springer Nature

SharedIt content-sharing initiative