Effects of S-Adenosylhomocysteine-Hydrolase Down-Regulation on Wnt Signaling Pathway in SW480 Cells

1. Introduction

S-adenosylhomocysteine hydrolase (AHCY) is an enzyme that catalyzes the hydrolysis of S-adenosylhomocysteine (SAH) to produce adenosine (Ado) and homocysteine (Hyc) [3]. SAH is generated through transmethylation reactions of S-adenosylmethionine (SAM), which serves as main methyl-group donor in most living organisms, and is involved in a variety of cellular processes, including DNA methylation, histone modification, and RNA processing. Also, SAH is a potent competitive inhibitor of methyltransferases [4]. Therefore, maintaining proper AHCY activity is crucial for regulating the cellular methylation potential, which is determined by the ratio of SAH to SAM metabolites [5,6]. The significance of AHCY in regulating the cellular methylation potential has been underscored by the discovery of AHCY deficiency in humans, a rare, and potentially lethal multisystem disorder caused by allelic mutations in the AHCY gene that result in reduced AHCY enzymatic activity [1,7,8,9,10,11], with dramatically increased levels of metabolites SAH and SAM. Evidently, increased levels of SAH are associated with increased cell proliferation, migration, and invasion, possibly due to disruption of methylation status and altered expression of key genetic factors that control these vital cellular processes [12]. In a previous study, we showed that DNA hypermethylation seems to be a frequent but not a constant feature associated with AHCY deficiency that affects different genomic regions to different degrees, which on the other hand could impair the regulation of gene expression, and subsequently cellular signaling pathways [13].

So far, AHCY deficiency has been linked to a range of metabolic disorders, including liver disease [14]. Also, AHCY downregulation contributes to tumorigenesis [15].

However, still elusive appear the pathogenic effects of AHCY deficiency at molecular level. Understanding the mechanisms that lead to aversive conditions and diseases as a result of AHCY deficiency is important for developing effective strategies for the prevention and treatment of these conditions. Thus, in this study we investigated the effects of AHCY down-regulation on the colon cancer derived model cell line SW480.

To do so, we enabled short hairpin RNA (shRNA) mediated gene silencing in SW480 cells, in order to resemble AHCY deficiency at cellular level. The efficiency of generating AHCY-deficient environment was evaluated by determining the concentrations of crucial metabolites SAM and SAH in the AHCY-deficient SW480 cells. Subsequently, after establishing effective AHCY silencing, we performed RNA sequencing to analyze changes in gene expression levels in response to AHCY down-regulation. To contextualize the RNA-seq data, we deployed Ingenuity Pathway Analysis (IPA) for predicting changes to signaling networks and related downstream effects, and to identify new targets or candidate biomarkers in context of various biological systems. We also performed western blotting to confirm data obtained from RNA sequencing, by assessing the protein levels of key components of signaling pathways of interest.

Besides the major role of AHCY in the regulation of the cellular methylation potential, we show that AHCY downregulation can impact a wide range of cellular functions. Our findings provide new insights into the molecular mechanisms underlying the effects of AHCY downregulation, in particular on, Lymphoidphoid enhancer-binding factor 1 (LEF1), a member of the T-cell Factor (TCF)/LEF1 family of high-mobility group transcription factors, and a downstream mediator of the Wnt/β-catenin signaling pathway. The Wnt pathway is highly conserved and involved in various cellular processes, including embryonic development, tissue homeostasis, stem cell maintenance, and cell differentiation. This result bodes well with previous findings that implicate disorder of Wnt signaling in various diseases, including cancer and neurodegenerative diseases [16]. On the other hand, LEF1 is essential in stem cell maintenance and organ development, especially in its role in epithelial-mesenchymal transition (EMT) [17]. Thus, investigation of this relationship may help elucidate the molecular mechanisms underlying the effect of AHCY on cancer cell behavior, and may contribute to a better understanding of the pathogenesis of AHCY deficiency-related diseases.

2. Results

2.1. SAM/SAH measurements

The levels of S-adenosylmethionine (SAM) and S-adenosylhomocysteine (SAH) were measured in AHCY deficient cells to investigate the impact of AHCY deficiency on the metabolism of these compounds. Compared to the control cells, the AHCY deficient cells exhibited a significant increase in the amount of SAH, with levels approximately two-fold higher. In the AHCY deficient cells, the concentration of SAH was found to be [4.6] ng/mL, whereas in the control cells, it was [2] ng/mL. This substantial elevation in SAH suggests impaired methylation capacity in the AHCY deficient cells, as SAH is an inhibitor of methyltransferase enzymes. Conversely, the levels of SAM, the precursor of SAH and a key methyl donor, did not show a significant difference between the AHCY deficient cells and the control cells. The concentration of SAM in the AHCY deficient cells was [278] ng/mL, while in the control cells, it was [458] ng/mL.

The appropriate concentration of puromycin (0.8 μg/mL) was used for selection of cells with shRNA insert after infection with lentiviral particles.

Figure 1. SAM/SAH measurements (c) Levels of SAM and SAH (ng/ml) and their ratio (d) SAM/SAH in the lysates of AHCY-silenced and control cells, as measured by LC-MS/MS. ± SD is represented as vertical line and is based on three independent measurements.

Figure 1. SAM/SAH measurements (c) Levels of SAM and SAH (ng/ml) and their ratio (d) SAM/SAH in the lysates of AHCY-silenced and control cells, as measured by LC-MS/MS. ± SD is represented as vertical line and is based on three independent measurements.

Figure 2. Cell Culture and Antibiotic Resistance Testing.

Figure 2. Cell Culture and Antibiotic Resistance Testing.

2.2. RNA-seq

mRNA sequencing of SW480 cells yielded an average of 65 million paired-end reads per sample, with a read length of 151 nucleotides. The reads were aligned to the reference genome (hg38 No Alts, with decoys)) using DRAGEN RNA Pipeline (Version 3.9.5.) available on BaseSpace Sequence Hub (Illumina), with an average mapping rate of 98,1% of total reads. RNA quantification was performed using the aforementioned BaseSpace tool, and differential expression analysis was performed using the DRAGEN Differential Expression tool (Illumina) that utilizes DeSeq2 algorithm to identify differential expresses genes between two conditions.

Overall, we identified approx. 15,000 expressed genes in SW480 cells, with 3627 genes showing significantly different expression levels between treated and control groups (adjusted p-value < 0.05 and fold change > 0.5). Of these differentially expressed genes, 1350 were upregulated and 2,277 were downregulated in the treated group compared to the control group.

2.3. In-depth Examination of the Most Significantly Altered Signalling Pathways

The IPA software package was used to analyse the data generated by mRNA sequencing, indicating significant differences in gene expression in several biological processes and pathways. We found 523 genes related to cellular movement, 605 related to cellular development, and 600 genes related to cellular growth and proliferation to be differentially expressed, respectively. (Table 1 and Table 2).

Further, we have focused our analysis to most relevant non-canonical signaling pathways that are somehow connected to differentially expressed LEF1 protein, such as the Wnt signaling network (Table 3, Figure 3), epithelial-mesenchymal transition (EMT) (Figure 4, Table 4), epithelial adherens junctions signaling (Figure 5), differential expression of cyclins and cell cycle regulation signaling (Figure 6), MYC network (Figure 7), tumor cell microenvironment pathway (Figure 8, Table 6), calcium signaling, STAT3 pathway ( Figure 9, Table 5), human embryonic stem cell pluripotency signaling (Figure 10), and Rho Family GTPases signaling (Figure 11). Also, we conducted data integration of differential gene expression, LEF1 Protein Levels, Wnt Signaling, and Cellular Responses (Figure 12).

2.4. Epithelial-Mesenchymal transition and E-cadherin/β-catenin/Wnt pathway

The AHCY deficient SW480 cell line exhibits significant changes in expression levels of genes that are part of the epithelial-mesenchymal transition (EMT). Most interesting changes occurred to the Lymphoidphoid enhancer-binding factor 1 (LEF1) gene expression. Accordingly, we have observed significant changes in Wnt signaling (Figure 3 and Figure 4, Table 3), which is to be expected as LEF1 is part of the Wnt/β-catenin signaling pathway.

Other changes within the Wnt pathway include WNT6 (Wnt family member 6) with a highly increased expression. Wnt6 is highly conserved in various species, mainly considered to be a member of the β-catenin-dependent Wnt signaling pathway.

Besides the upregulation of LEF1 and its possible regulatory role in Wnt signaling, we have observed the reduced expression of cadherin 12 (CDH12) (Table 3).

Also, we show increased ALCAM expression (Activated Leukocyte Cell Adhesion Molecule). The expression of ALCAM correlates with the expression of Snail proteins, showing Snail 1 and Snail 2 to be more active (Figure 5, Figure 7 and Figure 12, Table 4 and Table 5). Snail proteins are well-known EMT-Transcription factors, directly binding and suppressing E-cadherin at the proximal CDH1 promoter and remodeling intercellular adhesion.

Also, significant alterations in genes associated with tumor cell invasion were detected, such as significant upregulation of TGFβ1, ROAR, DAB2, BMP6, NOS2, PLXN2, and CADPS exhibited, and significant downregulation of TCF4 (Figure 12).

2.5. Differential Expression of Cyclins and Cell Cycle Regulation Signaling

The transcript levels of Cyclin A, Cyclin B, and CDK1 were found to be significantly increased (Figure 6). Cyclin B, in collaboration with CDK1, orchestrates the transition from the G2 phase to the mitotic phase, enabling successful cell division. Accordingly, we observed a higher level of gene expression of RB1, E2F, and TFDP1, which are key regulators of the cell cycle.

2.6. MYC, STAT3 and Human Embryonic Stem Cell Pluripotency Signaling

We have identified several proteins that are potentially regulated by MYC (Figure 7). Among them, the expression levels of AHCY, SOX5, SON, OLR1, LRG1, and COL4A1 were found to be significantly associated with MYC activity, specifically in terms of their potential downregulation. MYC exerts its regulatory role by directly binding to the regulatory regions of these genes and modulating their transcriptional activity.

Additionally, upregulation and activation of STAT3, MYC, CDC25A, and BCL2 is shown (Figure 9, Table 7), suggesting potential implications for enhanced cellular proliferation, survival, and anti-apoptotic responses.

2.7. The tumor microenvironment pathway

AHCY-deficient SW480 cells exhibit significant upregulation of genes important for the tumor microenvironment pathway, such as MMP19, MMP24, and CSF2, as well as upregulated activation of PLAU and BCL2 (Figure 8, Table 6).

We have observed higher expression of TIAM1 and activation of Rac1 (Figure 8). TIAM1 acts as a GEF, a protein that facilitates the exchange of GDP (guanosine diphosphate) for GTP (guanosine triphosphate) on Rac1.

2.8. G protein-coupled receptor (GPCR) signaling

RNA-seq data revealed an interesting observation regarding the expression of G protein-coupled receptor (GPCR) signaling. We found a downregulation of Adora receptors (GPCR) signaling in the AHCY- deficient cells (Figure 7), suggesting a potential alteration in cellular responses to external stimuli. This downregulation may place the tumor cells under increased stress, making them more competitive for limited resources and driving the activation of mechanisms responsible for cell migration and invasion.

2.9. Signaling by Rho Family GTPases

We observed activation of ROCK kinase, which further contributes to cytoskeletal remodeling and cell contractility (Figure 11). Namely, Rho GTPases, including Rho, PIP5K, and ROCK, are key regulators of cytoskeletal dynamics. The Rho signaling cascade also involves downstream effectors, such as FAK (focal adhesion kinase). Additionally, the increased activation of FAK (Figure 10), shown by its phosphorylated form, suggests enhanced focal adhesion turnover and cell-substrate adhesion including cytoskeleton reorganization.

2.10. Western blotting

Western blotting results confirmed efficient lentiviral mediated knock-down of AHCY gene expression in SW480 cells, with significantly decreased AHCY protein levels (Figure 13a).

Also, western blotting revealed a significant increase in the protein levels of LEF1 in AHCY-deficient SW480 cells when compared to the control cells (Figure 13b). Quantification of band intensities showed an approximately 50% increase in LEF1 protein expression in the AHCY- deficient cells. This observation indicates that the deficiency of AHCY has a direct impact on the expression of LEF1 in SW480 cells. Additionally, we found a significant increase in the expression of STAT3 protein in AHCY- deficient cells compared to control cells (Figure 13c).

For all experiments, GAPDH was used as a loading control, and detected by a rabbit polyclonal antibody (ab9458, Abcam). 30 μg of whole cell proteins retrieved from SW480 AHCY-deficient or SW480 control cells were loaded per well.

(a) Detection of AHCY protein using a monoclonal AHCY antibody (ab134966; Abcam).

(b) Detection of LEF1 protein using a monoclonal LEF1 antibody (sc-374522, Santa Cruz Biotechnology).

(c) Detection of STAT3 protein using a monoclonal STAT3 antibody (sc-8019, Santa Cruz Biotechnology).

3. Discussion

In order to imbalance the cellular SAM-to-SAH ratio and inflict changes to the cellular methylation potential, we knocked-out endogenous AHCY in the model cell line SW480, which leads to an accumulation of SAH. The elevated levels of SAH in the AHCY deficient cells may have implications for cellular processes that rely on proper methylation, such as gene expression regulation and epigenetic modifications.

Indeed, we have establihed a new link between S-adenosylhomocysteine hydrolase and cancer cell signaling by analyzing differentially expressed pathways in AHCY deficient SW480 cells. Namely, after AHCY knock-down, these cells exhibit significantly increased LEF1 protein levels, placing LEF1 into a complex interplay of various signaling pathways and molecular players involved in tumor cell migration and invasion, in particular the Wnt signaling pathway, where the up-regulation of LEF1 possibly is disrupting the TCF/LEF transcription factors ratio [4].

Namely, LEF1 is part of the Wnt/β-catenin signaling pathway, which includes for example genes such as c-Myc, LBH, Oct4, NANOG that have been associated with the upregulation of proteins typically involved in human breast cancer, gastrointestinal tumors, prostate cancer, leukemia, and others [18,19,20,21,22,23,24,25,26,27,28,29]. Some of these genes confer stem cell qualities including c-myc, cyclin D1, Oct4, and NANOG, and siRNA-mediated knockdown initiates differentiation [30,31], respectively. In addition, c-Myc appears to serve as a master regulator, playing a critical role in in embryonic development and regulating the transcription of genes involved in the cell cycle, and targets molecules involved in the G1/S transition such as CDK2, CDK4, CDC25A, and E2Fs [32].

Cyclin D1 is involved in cell cycle progression, especially in the G1 phase, and is necessary for growth and proliferation [33]. They also serve as downstream effectors of Wnt signaling and are activated by the recruitment of LEF1 to their respective promoter sites. Namely, the promoters for c-myc and cyclin D1 contain LEF1 consensus sequences that allow β-catenin-LEF1 to bind and modulate c-myc and cyclin D1 expression [34,35,36,37]. Other downstream target genes involved in proliferation could be affected as well, such as survin, and VEGF [55,56]. Point mutations of LEF1 located in exons 2 (K86E) and 3 (P106L) of LEF1 result in increased promoter activity and expression for c-myc and cyclin D1, causing increased leukemia cell proliferation [38]. Altered LEF1 expression and function commonly occur in several cancers, such as lung adenocarcinoma, colon cancer, endometrial carcinoma, prostate cancer and leukemia [39,40,41,42,43,44,45]. High LEF1 and low Notch2 expression patterns are associated with tumorigenesis, shorter overall survival time, and higher risk of death in CRC patients [46,47]. Also, the presence of increased LEF1 is associated with an increased risk for primary colorectal cancer and liver metastasis [48]. On the other hand, knockdown of LEF1 in colon cancer cells results in various effects on cellular processes such as a) increased apoptosis compared to control cells in vitro, and reduced tumor growth compared to normal colon cancer cells in vivo, b) reduced invasiveness via decreased MMP-2 and MMP-9 expression, and c) changed expression of genes involved in regulation of expression of matrix metalloproteinases such as metallopeptidase 7 (MMP7, a Zn²⁺ - dependent proteolytic enzyme) [49]. These studies demonstrate the importance of LEF1 in elucidating typical cancer characteristics, including proliferation, invasion, migration, and viability, amongst a variety of cancer types, and highlight its necessity in propagating these effects. Not only is LEF1 at the center of signaling pathways and mechanisms that initiate and maintain carcinogenesis, suppression of LEF1 reduces the proliferative and invasive properties of cancer. Also, LEF1 has been reported to promote EMT in cancer cells by activating Wnt/β-catenin signaling, which can in turn activate Notch signaling and GPCR signaling [50]. Similarly, calcium signaling has been shown to regulate Wnt/β-catenin signaling by activating calcium/calmodulin-dependent protein kinase II (CaMKII), which can phosphorylate LEF1 and activate its transcriptional activity. Therefore, LEF1 may indirectly modulate the activity of several of the signaling pathways identified in the RNA-seq analysis by interacting with other key signaling molecules, such as β-catenin, Notch, and GPCRs. This assumption is fostered by finding significantly lower expression levels of TCF4, whereas TCF4 is known to be in a direct interaction with LEF1 known as an interaction TCF/LEF, where overexpressed LEF1 leads to an enhanced tumor cell invasiveness and induces epithelial to mesenchymal transition [51]. Namely, transcription of LEF1 can be directly regulated by TCF4-β-catenin complexes [52]. As LEF-1 is not expressed in the normal colon mucosa [53], but is found in human colorectal cancer [54], a shift of β-catenin binding partners from TCF4 to LEF-1 might occur during carcinogenesis which might enable enhanced epithelial-mesenchymal transition (EMT) and malignant progression.

The precise mechanisms and consequences of these interactions may vary depending on the cellular context, specific target genes, and extracellular signals present. Further investigations are necessary to fully elucidate the intricate interplay between LEF1 and these canonical and non-canonical pathways in the context of our study, considering their potential impact on gene expression and cellular processes.

3.1. Additional Pathways Perturbations3.2. Epithelial-Mesenchymal transition

So far, the hallmark of EMT is the loss of epithelial marker expression, typically indicated by the presence of E-cadherin, with a gain in mesenchymal marker expression such as of N-cadherin and vimentin accompanied by invasive phenotype. Therefore, the E-cadherin/β-catenin/Wnt pathway signaling is pivotal for comprehending the potential consequences of identified gene expression alterations, given the observed changes in Wnt signaling, and its central role in governing cell-cell adhesion and the regulation of cell proliferation—both frequently perturbed within the tumor microenvironment. As we have found WNT6, highly conserved in various species, with a highly increased expression, and mainly considered to be a member of the β-catenin-dependent Wnt signaling pathway [55], we assume that Wnt6 might increase the proliferative ability of colorectal cancer cells (CRC), leading to increased expression of MMP2, which is also involved in the breakdown of the extracellular matrix [56]. In addition, the promoter region of Wnt6 is bound by polymorphic adenoma-like protein 2 (PLAGL2) in the nucleus of CRC cells [57]. PLAGL2, a zinc finger protein derived from the PLAG gene family [58,59,60], is a proto-oncogene and a transcription factor. PLAGL2 combines with the Wnt6 promoter and activates the β-catenin-dependent Wnt signaling pathway, thereby stimulating various downstream target genes (such as MMP7, CCND1) and promoting tumor development [61,62,63,64,65].

E-cadherin together with β-catenin as an adaptor protein establishes links to the actin cytoskeleton. Under physiological conditions, cytoplasmic β-catenin remains in an inactive state by being bound to the APC/GSK3β/Axin/CK1 degradation complex and undergoes phosphorylation for ubiquitination. Wnt signaling inhibits this degradative process by phosphorylating and inhibiting the GSK3β complex. Under conditions that amplify aberrant Wnt signaling, β-catenin translocates into the nucleus and binds to TCF-4/LEF-1 proteins to induce Wnt target genes such as c-Myc, cyclins, MMP, etc., leading to uncontrolled cell proliferation and growth [61,62,63,64,65]. In the absence of E-cadherin, un-sequestered β-catenin released from the membrane-bound cadherin-catenin complex leads to excess cytoplasmic β-catenin. It has been demonstrated that β-catenin uses the same binding interface to engage TCF and E-cadherin ligands and cadherins have a superior binding affinity. There is a suggestion that as E-cadherin protein is lost, there is excess un-sequestered cytoplasmic β-catenin that escapes degradation and enters the nucleus to bind to TCF and activate Wnt pathway. In addition to activating downstream Wnt associated genes, it is also demonstrated that nuclear translocation of β-catenin represses PTEN expression. PTEN is a tumor suppressor and a critical regulator of AKT/MTOR pathway. Thus, the carefully balanced Wnt/β-catenin/E-cad functioning is tipped in favor of uncontrolled cell proliferation promoting oncogenesis [93–100]. In addition, important signaling interactions between E-cadherin and other cellular pathways include RTK/EGFR/MAPK, and the P-120/Rho/RAC pathway, respectively.

Besides the upregulation of LEF1, and being part of the Wnt/β-catenin signaling pathway with possible regulatory roles in aformentioned processes, we have observed the reduced expression of cadherin 12 (CDH12), that has been implicated in promoting increased metastatic potential and cell migration [66]. The loss or downregulation of CDH12 can disrupt the adhesive interactions between cells, leading to a decreased cohesive behavior within the primary tumor and facilitating the detachment of tumor cells from the primary site. This loss of cell adhesion can enhance the migratory capacity of cancer cells, enabling their invasion into surrounding tissues and dissemination to distant sites. Consequently, the decreased expression of CDH12, along with reduced adhesive molecules, may contribute to a more aggressive and metastatic phenotype in cancer cells [67].

Contributing to potential migratory capacity might be enhanced expression of ALCAM, which is involved in cancer cell migration, in conjunction with the activation of the EMT pathway. ALCAM facilitates interactions between cells and their surrounding environment, and influences cytoskeletal rearrangements, promoting cellular protrusions that expedite directed cell migration. The expression of ALCAM correlates with the expression of SNA1, which is evident in our data [68].

Snail family members (Snail [SNAI1] are well-known EMT-TFs. The EMT-TFs directly bind and suppress E-cadherin at the proximal CDH1 promoter and remodel intercellular adhesion. Snail also suppress other epithelial markers and activate mesenchymal genes and we see in our data Snail 1 and Snail 2 more active (Figure 4, Table 5). Furthermore, EMT-TFs are known to reorganize epithelial polarity molecules and impede basement membrane formation to promote pro-invasive circumstances [69-70.

Significant upregulation of expression is found for genes associated with tumor cell invasion such TGFβ1, ROAR, DAB2, BMP6, NOS2, PLXN2, and CADPS, whereas TCF4 was significantly downregulated in AHCY deficient cells. Thus, our data bode well for linking gene activity changes to tumor progression and metastasis, suggesting that, a) TGFβ1's elevated activity aligns with invasion promotion, b) ROAR's surge suggests heightened invasiveness, c) DAB2's rise echoes invasion dynamics, d) BMP6's elevation points to tumor progression, e) NOS2's increase connects to invasiveness, f) PLXN2's upregulation implies migration involvement and, g) CADPS's rise aligns with invasiveness [71,72,73,74,75,76].

Interestingly, in the context of cell migration and invasion, we found downregulation of Adora receptors (GPCR), suggesting a potential alteration in cellular responses to external stimuli. This downregulation may place the tumor cells under increased stress, making them more competitive for limited resources and driving the activation of mechanisms responsible for cell migration and invasion.

Additional cytoskeletal dynamics are possibly mediated through signaling by Rho Family GTPases, fostered by the observed activation of ROCK kinase, which contributes to cytoskeletal remodeling and cell contractility. Namely, Rho GTPases, including Rho, PIP5K, and ROCK, are key regulators of cytoskeletal dynamics, including downstream effectors, such as FAK (focal adhesion kinase), which exhibits increased activation suggesting s enhanced focal adhesion turnover and cell-substrate adhesion including cytoskeleton reorganization [77,78]. Thus, the activation of Wnt signaling, STAT3, Rho GTPases, ROCK kinase, FAK, Regulation of the EMT Pathway and the upregulation of LEF1 protein collectively might contribute to cytoskeletal reorganization, cell trafficking, and enhanced cell motility, and highlight the complicated network of molecular events involved in the invasive behavior of tumor cells. Calcium signaling can intersect with the non-canonical Wnt pathway, potentially affecting LEF1 through various mechanisms, while LEF1, as a downstream effector of the canonical Wnt pathway, plays a role in regulating Epithelial-Mesenchymal Transition (EMT). Additionally, G Protein-Coupled Receptor (GPCR) signaling can influence the canonical Wnt pathway [79,80,81].

3.3. Differential Expression of Cyclins and Cell Cycle Regulation Signaling

Increased transcript levels of Cyclin A, Cyclin B, and CDK1 are indicating a potential modulation of cell cycle dynamics in response to AHCY deficiency. Cyclin B, in collaboration with CDK1, orchestrates the transition from the G2 phase to the mitotic phase, enabling successful cell division [82]. The increased expression of Cyclin B and CDK1 implies an augmented drive toward mitosis, possibly reflecting a compensatory mechanism triggered by AHCY deficiency, supported by higher expression of RB1, E2F, and TFDP1, which are key regulators of the cell cycle, being in a more active state in the studied cells. Considering the increased activity of RB1, E2F, and TFDP1, it can be inferred that these molecules are facilitating cell cycle progression.

RB1, when inactive or phosphorylated, releases its inhibitory effect on E2F transcription factors. The active E2F factors, in turn, promote the transcription of genes involved in DNA replication and cell division. TFDP1 interacts with E2F, forming the E2F/TFDP1 complex, which enhances the transcriptional activity of E2F. This complex further promotes the expression of genes required for cell cycle progression [83].

The activation of the E2F/TFDP1 complex leads to the transcription of genes involved in various phases of the cell cycle, such as G1/S transition, S phase, G2 phase, and M phase. However, it is unclear whether the dysregulated cyclin signaling observed in AHCY deficient SW480 cells may be influenced by aberrant LEF1 activity and its interplay with the Wnt pathway.

3.4. MYC, STAT3 and Human Embryonic Stem Cell Pluripotency Signaling

Significant perturbations in expression levels have been observed for several proteins that are potentially regulated by MYC, but also MYC itself. Namely, MYC exerts its regulatory role by directly binding to the regulatory regions of distinct genes such as SOX5, SON, OLR1, LRG1, and COL4A1, which have been found to be significantly associated with MYC activity, specifically in terms of their potential downregulation. Proteins SOX5, SON, and OLR1 have been implicated in various aspects of cancer progression and metastasis [81]. SOX5, a paralog of SOX2 within the SOX transcription factor family, may play a significant role in regulating cellular processes in the AHCY deficient SW480 cell line. Furthermore, the presence of such connections in other cellular systems and the known involvement of SOX6, a paralog of SOX2, in Wnt signaling, strengthens the importance of these findings within the broader context of cell regulation and signaling pathways in the tumor microenvironment. In the case of COL4A1, which is a component of basement membranes, lower expression could potentially disrupt the integrity of basement membranes. Our study also explores the connection between AHCY deficiency and pluripotency signaling in ESCs, focusing on observed upregulation of STAT3, and MYC, together with CDC25A, and BCL2, that suggest potential implications for enhanced cellular proliferation, survival, and anti-apoptotic responses. Also, as MYC, CDC25A, and BCL2 show increased activity, one might argue regarding possible contributions to cell growth, cycle control, and resistance to cell death as shown elsewhere [84,85,86,87,88]. Furthermore, the connection between STAT3 signaling and LEF1 protein levels suggests a potential interplay between these pathways, possibly mediated through crosstalk with the Wnt signaling pathway. Namely, STAT3 activation is crucial for ESC self-renewal by controlling important pluripotency genes like OCT4, NANOG, and SOX2. Additionally, STAT3 influences ESC growth and proliferation by affecting genes involved in the cell cycle, including cyclin D1 and c-myc. The downstream factor MYC also plays a role in ESC development, cell growth, proliferation and maintenance of pluripotency [89]. Therefore, our analysis establishes also links between AHCY deficiency and pluripotency signaling, shedding new light on the interaction of STAT3 and downstream elements in the HESCPS network.

3.5. The tumor microenvironment pathway

Significant changes have been detected in the tumor microenvironment pathway including genes MMP19, MMP24, and CSF2, as well as PLAU, BCL2, TIAM1 and Rac1. These changes might be attributed to the increased LEF1 protein levels, suggesting potential implications for the modulation of the tumor microenvironment in response to AHCY deficiency.

In addition, CSF2, also known as GM-CSF (Granulocyte-Macrophage Colony-Stimulating Factor), is a cytokine that plays a crucial role in the regulation of immune cell development, function, and inflammation. In addition to its immunomodulatory functions, emerging evidence suggests that CSF2 also contributes to cancer cell migration and invasion in the context of cancer metastasis, and indeed we detected highly increased expression of CSF2. In epithelial ovarian cancer cells activation of the CSF2/p-STAT3 pathway leads to the enhancement of stem cell-like properties in cancer cells [90]

TIAM1 acts as a GEF, a protein that facilitates the exchange of GDP (guanosine diphosphate) for GTP (guanosine triphosphate) on Rac1. This exchange shifts Rac1 into its active, GTP-bound form, triggering downstream signaling pathways that promote cell migration. Within the existing scientific literature, there is a firmly established comprehension of TIAM1's integral role in cell proliferation and tumorigenic potential [88].

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Loss of TIAM1 or RAC1 inhibition induces cell death via BAX/BAK-mediated apoptosis. TIAM1-Nur77 interaction is required for small cell lung cancer (SCLC) cell survival. [91].

In addition, TIAM1 was elevated in thyroid cancer, and TIAM1 knockdown repressed thyroid cancer cell proliferation and promoted ferroptosis through regulating Nrf2/HO-1 axis. Taken together, these findings may suggest that TIAM1 plays a significant role in the tumor microenvironment signaling pathway by activating cell proliferation and tumorigenic potential.

Taken together, our findings provide valuable insights into the complex interplay of various signaling pathways and molecular players involved in tumor cell migration and invasion. Understanding these mechanisms at a molecular level may pave the way for the development of targeted therapeutic interventions aimed at disrupting these pathways and inhibiting tumor metastasis. Further investigations into the precise molecular mechanisms underlying the observed alterations and their implications for tumor progression will be crucial for a comprehensive understanding of tumor biology.

4. Materials and Methods

4.1. Cell Culture:

SW480 cells were obtained from the American Type Culture Collection (ATCC) and cultured in DMEM (Dulbecco's Modified Eagle's Medium, Gibco) supplemented with 10% fetal bovine serum (FBS, Gibco) and 1% penicillin-streptomycin (Gibco) at 37°C in a humidified atmosphere with 5% CO₂.

HEK293T cells were obtained from the American Type Culture Collection (ATCC) and cultured in DMEM (Dulbecco's Modified Eagle's Medium, Gibco) supplemented with 10% fetal bovine serum (FBS, Gibco) and 1% penicillin-streptomycin (Gibco) at 37°C in a humidified atmosphere with 5% CO₂.

Cells were sub-cultured every 2-3 days and passages 5-10 were used for all experiments.

4.2. Viability Assay:

Cell viability was determined using the MTT assay. Briefly, cells were seeded at a density of 5×10^3 cells/well in 96-well plates and incubated for 24 h. The cells were then treated with different concentrations of the test compounds for 24 h. After treatment, MTT reagent (Sigma) was added to each well and the plates were incubated for an additional 4 h. The formazan crystals were solubilized with DMSO (Sigma) and the absorbance was measured at 570 nm using a microplate reader (Biotek).

4.3. Lentivirus Production:

HEK293T cells were seeded in a 10-cm dish at a density of 5x10^6 cells/dish and incubated overnight. The cells were transfected with 5 μg of the short hairpin RNA (shRNA) lentiviral vector plasmids shRNA2 and, shRNA4 (Sigma), both targeting AHCY, respectively, and helper plasmids psPAX2 (3.75 μg) and pMD2.G (1.25 μg) using Lipofectamine 3000 (Thermo Fisher Scientific) according to the manufacturer's instructions. For production of control cells we used SHC016 non-target shRNA plasmid (2 and 4, Sigma), in combination with psPAX2 and pMD2.G plasmids. After 24 h, the transfection medium was replaced with fresh medium. The supernatant containing functional lentiviral particles was collected 48 h and 72 h post-transfection, pooled, and filtered through a 0.45-μm syringe filter (Millipore).

4.4. Cell Culture and Antibiotic Resistance Testing

SW480 cells were cultured in DMEM media supplemented with 10% fetal bovine serum (FBS) and 1% penicillin-streptomycin in a humidified incubator at 37°C and 5% CO₂. Prior to lentiviral transduction, cells were tested for antibiotic resistance to puromycin using the MTT assay. Cells were seeded at a density of 2,000 cells per well in a 96-well plate and incubated for 24 hours to allow for cell attachment. Puromycin was added at various concentrations (0.2, 0.4, 0.6, 0.8, 1 μg/mL) and incubated for 48 hours. Following incubation, the media was removed, and the cells were washed with phosphate-buffered saline (PBS). MTT solution (5 mg/mL) was added to each well and incubated for 4 hours at 37°C. The MTT solution was removed, and the formazan crystals were dissolved in dimethyl sulfoxide (DMSO). Absorbance was measured at 570 nm using a microplate reader (Biotek). The concentration of puromycin that resulted in 50% inhibition of cell growth (IC₅₀) was determined using Excel.

4.5. Lentiviral Transduction:

SW480 cells were seeded in a 6-well plate at a density of 2x10^5 cells/well and incubated overnight. The cells were then transduced with the lentiviral vector containing the gene of interest and an antibiotic resistance gene using polybrene (Sigma-Aldrich) at a final concentration of 8 μg/mL. After 24 h, the transduction medium was replaced with a fresh medium containing the appropriate antibiotic (e. puromycin) at a concentration of 1 μg/mL. The cells were then cultured for 3 days to allow for the selection of transduced cells.

4.6. Transcriptome profiling – RNA-Seq

Total cell RNA was extracted from 1 × 10⁶ cells using TRIzol® Reagent (Thermo Fisher Scientific, 15596026) following the manufacturer’s instructions. Two different cell passages were used to extract RNA both for shAHCY and shCTRL cells and treated as a biological replicate. RNA quantity was determined using a Qubit 3.0 Fluorometer and Qubit® RNA BR Assay Kit (Thermo Fisher Scientific, Q33216, Q10211). Agilent 2100 Bioanalyzer and Agilent RNA 6000 Nano Kits (Agilent technologies, G2939AA, 5067-1511) were used to assess the sample quality. TruSeq Stranded mRNA Library Prep Kit (Illumina, NP-202–1001) was used to prepare libraries from 90 ng of total RNA. Collected libraries were analyzed on a Bioanalyzer 2100, diluted to 1.4 pM, and sequenced on an Illumina NextSeq500 System using NextSeq500/550 High-Output v2 Kit, with 75 cycles (Illumina, FC-404–2005). Run setup, direct data streaming, demultiplexing, and analysis were performed at the BaseSpace Sequence Hub (Illumina) using the RNA Express BaseSpace App with default analysis parameters. Signaling pathway analysis was done by Ingenuity Pathway Analysis software (IPA, Ingenuity Systems; see http://www.ingenuity.com) The IPA Core Analysis was run with the Causal Network analysis option on the uploaded datasets for transcriptome data. Additional relevant parameters include the measurement value type for transcriptome log₂ (fold change), a cut-of range: −0.5–0.5; focus on: both up/down-regulated, and species: human. The p-value was calculated using the right-tailed Fisher’s exact test.

Western blotting Whole-cell proteins were obtained by cell scraping in cold lysis buffer. The pellet is resuspended in 300 µL of a previously prepared cell lysis buffer RIPA (150 mM NaCl, 50 mM Tris, 0.1% SDS, 0.5% sodium deoxycholate (DOC), 1% NP-40) supplemented with protease inhibitors cOmplete™ Mini Protease Inhibitor Cocktail (SIGMA) and phosphatase inhibitor sodium orthovanadate Na₃VO₄ (Thermo Scientific) at a final concentration of 1 mM. Following sonication on ice (Misonix XL2000 Microson, 5.5 settings). After centrifugation at 14,000 rpm at +4 °C. The protein concentration in the supernatant was determined using a Pierce BCA Protein Assay Kit (Thermo Fisher Scientific, 23225). Proteins were separated by SDS-PAGE electrophoresis and transferred onto nitrocellulose membranes using the Trans-Blot® TurboTM Transfer System (BIO-RAD) and Mini Nitrocellulose Transfer Packs (BIO-RAD) according to the manufacturer's recommendations with turbo settings for transferring proteins of a wide range of molecular weights. To verify the successful transfer and quantify total proteins on the membrane, the proteins were briefly stained with a Ponceau S solution (0.1% Ponceau S, 5% acetic acid) and then rinsed with TBS-T buffer (Tris Buffered Saline Tween; 50 mM Tris-HCl (pH 7.5), 150 mM NaCl, 0.1% Tween-20). The membrane was then blocked with blocking buffer (5% non-fat milk powder in TBS-T, Sigma) for 1 hour at room temperature (RT) and washed 3 times for 15 minutes with TBS-T buffer (Sigma). The appropriate primary antibodies, e.g. anti-AHCY (ab134966 Abcam), anti-LEF1 (sc-374522, Santa Cruz Biotechnology), anti-STAT3 (sc-8019, Santa Cruz Biotechnology), and anti-GAPDH (ab9458, Abcam) were diluted in blocking buffer according to the manufacturer's recommendation, and the membranes were incubated with it for 2 hours at RT or overnight at +4oC, respectively. The membranes were washed 3 times for 15 minutes with wash buffer, and the mouse IgG Fc binding protein Horseradish Peroxidase (HRP) conjugated secondary antibody (sc-525409, Santa Cruz Biotechnology), diluted in blocking buffer according to the manufacturer's recommendation, was added for incubation at RT for 1 hour for STAT3 and LEF1 membranes. Accordingly, the HRP conjugated secondary Goat Anti-Rabbit IgG H&L antibody (ab6721, Abcam) was used for AHCY and GAPDH detection, respectively. Afterwards, the membranes were washed 3 times for 15 minutes with wash buffer. The chemiluminescent signal was developed using the ClarityTM Western ECL Blotting Substrate (BIO-RAD) kit according to the manufacturer's recommendation and detected using Alliance Q9 Mini (UVITEC). Densitometry analysis of the signal on the membrane images was performed using the ImageJ software.

Determination of SAM and SAH by LC–MS/MS. The liquid chromatography linked to the tandem mass spectrometry method for the determination of S-adenosylmethionine (SAM) and S-adenosylhomocysteine (SAH) in human cells was developed as a modification of a previously published method by Kozich laboratory. Namely, instead of using perchloric acid, we modified procedures in favor of ammonium formate. Stock solutions of SAM and SAH were prepared as 1 mg/mL solutions in cold Milli-Q water (MERCK MILLIPORE, Synergy). Since SAM and [2 H3]-SAM were obtained as p-toluene sulfonate salts, exact concentrations were determined by UV-spectroscopy (molar extinction coefficient ε=15400). Aliquots (0.1 mL) of SAM and SAH stock solutions were diluted together in Milli-Q water to yield concentrations of 10 μg/mL for each substance. [2 H3]-SAM was diluted with 0.1% FA in Milli-Q water. [13C5]-SAH was obtained from 30.3 μg/mL solution in Milli-Q water. Spiked solutions of labeled SAM and SAH were prepared by dilution of [2 H3]-SAM in Milli-Q water and with the addition of [13C5]-SAH. All standard solutions were stored at −20 °C. The calibration samples were prepared from SAM and SAH stock solutions in 1 M trifluoroacetic acid (TFA) with the addition of isotope-labeled internal standard solution (10 μL of spike solution [2 H3]-SAM 7.54 μg/mL and [13C5]-SAH 2.66 μg/mL). The calibration samples were neutralized with a 5 M solution of ammonium formate just before analysis, and the total volume of each calibration sample was 300μL. The particular calibration points of SAM and SAH were 11, 28, 56, 111, 333, 833, 1333, and 1666 ng/mL. QC samples were prepared as 333 ng/mL of 1 M TFA/5 M ammonium formate solution SAM and SAH spiked with the same volume of internal standard solution. The calibration curve was obtained by linear regression, and the peak area ratio (analyte/internal standard) was plotted versus the analyte concentration. Cells were grown in 10-cm plates, and after reaching 80% confluence, they were briefly washed in PBS. Lysis was performed in 1 M TFA on ice. The homogenate was briefly sonicated and cleared after centrifugation at 16 000 × g for 10 min at +4 °C. A 100-µL aliquot of cellular homogenate was spiked with internal standard solution (10μL of spike solution [2 H3]-SAM 7.54 μg/mL and [13C5]-SAH 2.66 μg/mL). Samples were neutralized with 190 µL of 5 M solution of ammonium formate just before analysis, and a clear solution was injected into the LC column. LC–MS/MS analysis was carried out using an Agilent Technologies 1200 series HPLC system equipped with a binary pump, a vacuum membrane degasser, an automated autosampler, and an injector interfaced with a 6420 triple quadrupole mass spectrometer with an electrospray ionization source (ESI; Agilent Technologies Inc. Palo Alto, CA, USA). The separation was performed on a Kinetex C18 column (75×4.6 mm, 2.6 μm particle size) (Phenomenex, Torrance, USA). Solvents for the analysis were 0.1% FA in water (solvent A) and 0.1% FA in acetonitrile (solvent B). The gradient was applied as follows: 0 min at 100% A, 0–3 min at 100% A, 3–12 min at 100% A-10% A, 12–14 min at 10% A, 14–16 min at 10% A-90% A, and 16–25 min at 100% A. The flow rate was 0.3mL/ min. Between 2.5 and 15 min of each run, the eluent was diverted to the ion source, while at the beginning and end of the run, the eluent was diverted to the waste. The ESI was operated in a positive mode, and samples were detected in the multiple reaction monitoring (MRM) modes, with a dwell time of 200 ms per MRM transition. The desolvation gas temperature was 300 °C, with a flow rate of 8.0 L/min. The capillary voltage was 4.0 kV. The collision gas was nitrogen. The MRM transitions of the precursor to product ion pairs were m/z 399.3-250.3 for SAM, m/z 402.3-250.3 for [2 H3]-SAM, m/z 385.3-136 for SAH, and m/z 390.3-136.3 for [13C5]-SAH. The fragment voltage for SAM and [2 H3]-SAM was 100 V and the collision energy was set at 10 V. The fragment voltage for SAH and [13C5]-SAH was 100 V and the collision energy was set at 15 V. All data acquisition and processing were performed using Agilent MassHunter software.

Data Upload and Core Analysis: The preprocessed gene expression data were uploaded into IPA for pathway analysis using the Core Analysis module. Core Analysis integrates known biological pathways, molecular networks, and functional annotations to analyze the input data.

The IPA Core Analysis was run with the Causal Network analysis option on the uploaded datasets for transcriptome data, providing single datasets. Molecule Identification: IPA mapped the gene symbols from the uploaded data onto its knowledge base to identify the corresponding molecules. This step aimed to ensure that the input genes were correctly annotated and matched with the existing biological information.

Pathway Analysis: IPA performed Pathway Enrichment Analysis using Fisher's exact test to determine the statistical significance of pathway enrichment based on the input gene expression patterns. A p-value threshold of (-0.5,0.5) was used to identify significantly enriched pathways. Functional Analysis: IPA conducted functional analysis to identify the biological functions, diseases, and upstream regulators associated with the input dataset. This analysis involved the prediction of activation or inhibition of regulatory molecules based on the input data and known downstream effects.

Interpretation and Visualization: IPA's visualization tools, including pathway maps, network diagrams, and functional analysis results, were used to interpret and visualize the results of the pathway analysis. These visualizations aided in understanding the underlying biology and generating hypotheses.

Statistical Analysis in IPA: Statistical significance in pathway enrichment and functional analysis results was determined using appropriate statistical tests: Fisher's exact test and z-score calculation. Multiple testing corrections Bonferroni were applied to control for false discovery rate where applicable.

4.7. Statistical Analysis:

All data are presented as mean ± standard deviation (SD) of at least three independent experiments. Statistical analysis was performed using GraphPad Prism software (version 9.0.1) and differences between groups were analyzed using one-way ANOVA followed by Tukey's multiple comparison test. A p-value of less than 0.05 was considered statistically significant.

5. Conclusions

Our study investigated the impact of the knock-down of S-adenosyl homocysteine hydrolase (AHCY) on gene expression and subsequent changes on signaling pathways. in the model cell line SW480.

Our findings demonstrate that AHCY deficiency significantly affects the levels of LEF1 protein in SW480 cells, leading to metabolic and signaling shifts causing gene expression changes with broad effects on Wnt signaling, EMT, Cell Cycle Regulation Signaling, and the tumor microenvironment pathway with potential implications for enhanced proliferation, cell invasion and metastasis.The canonical Wnt pathway and LEF1 indicate interplay on several signaling levels, and demonstrate that LEF1 plays a crucial role in cancer survival and activity:

• Calcium Signaling: Calcium signaling can intersect with the non-canonical Wnt pathway through various mechanisms. Calcium ions can modulate the activity of Wnt signaling components, including LEF1, by affecting β-catenin stability, the interaction between β-catenin and LEF1, or downstream signaling events. The PCP pathway after Wnt activation is also responsible for gene expression regulation but also for cell cytoskeleton remodeling together with ROCK and JNK kinases. The WNT/Ca2+ pathway is associated with muscle contraction, gene transcription, and enzyme activation and activates both β-catenin-dependent and β-catenin-independent pathways.
• Epithelial-Mesenchymal Transition (EMT): LEF1, as a downstream effector of the canonical Wnt pathway, can participate in the regulation of EMT. EMT is a dynamic process involved in tissue remodeling and cancer progression. Activation of the canonical Wnt pathway, including the involvement of LEF1, has been linked to the induction or maintenance of EMT programs.
• G Protein-Coupled Receptor (GPCR) Signaling: GPCR signaling can intersect with the canonical Wnt pathway through various mechanisms. Wnt ligands can be activated by GPCRs kinases, leading to the activation of downstream signaling cascades, which can modulate the canonical Wnt pathway and potentially influence the activity of LEF1, and we see in our RNAseq data lower differential expression of GPCRs.

Our findings provide valuable insights into the molecular mechanisms underlying cancer progression and may contribute to the identification of potential therapeutic targets for intervention in AHCY-deficient cancer cells.