Palmitate-Induced IRE1–XBP1–ZEB Signaling Represses Desmoplakin Expression and Promotes Cancer Cell Migration

The loss of cell adhesion junctions has been recognized as a hallmark of cancer and an important first step towards the metastatic progression of cancer cells (1). Mounting evidence suggests the epithelial-to-mesenchymal transition (EMT) program is responsible for the transcriptional repression and breakdown of the components of cell adhesion junctions driving tumor toward malignancy (2). This program is controlled by a number of key transcription factor families including SNAIL, TWIST, and ZEB (3, 4). These transcription factors suppress the expression of key cell adhesion components, like E-cadherin (CDH1), that are required for the proper assembly of cell adhesion junctions (5). Recent studies have established that the EMT program can be invoked in cancer cells in the presence of elevated palmitate (PA) levels due to increased expression of fatty acid uptake molecules like CD36 (6, 7). In vitro studies have shown that treating cancer cell lines with PA results in the loss of desmoplakin (DSP) expression (8, 9). DSP serves as an obligate component of desmosomes that function as intercellular adhesion junctions and sites for intermediate filament attachment (10). Besides maintaining the integrity of desmosomes, DSP also plays an important role as tumor suppressor (11–13) by regulating various signaling pathways in cancer cells (14–16). However, the precise mechanism that leads to the loss of DSP expression in PA-treated cells is unknown.

In the presence of elevated PA, accumulation of unfolded proteins and other cellular stresses trigger the endoplasmic reticulum (ER) stress response (17). Three transmembrane ER signaling proteins PERK [EIF2AK3; protein kinase R (PKR)–like ER kinase], IRE1α (ERN1; inositol-requiring enzyme), and ATF6 (activating transcription factor 6) function as critical sensors of ER stress leading to the activation of unfolded protein response (UPR; ref. 18). A number of signaling pathways, including apoptosis and cell death, are controlled by the UPR that affect the survival outcomes of cells in a dose-dependent manner (19, 20). Research from our group and others established that PA induces ER stress in mammalian and yeast cells via the IRE1 branch (21–24). It is well established that the IRE1 branch of UPR response detects unfolded proteins and chaperones via the IRE1α luminal domain, while it senses membrane lipid saturation via its transmembrane domain (21–24). The self-association and dimerization of IRE1α subsequently activates the RNase domain (RD; ref. 25). This active form of IRE1α splices the mRNA of the X-Box binding protein-1 (XBP1) to produce the spliced XBP1 transcription factor (XBP1^S) that upregulates genes involved in enhancing ER protein-folding capacity (26, 27). Besides regulating expression of stress response–related transcripts, XBP1 can also bind to the promoters of EMT transcription factors like SNAIL1, ZEB2, and TCF3 (28).

On the basis of this link, we hypothesized that the activation of the IRE1–XBP1 pathway triggers the loss of DSP in cancer cells via the ZEB transcription factors. We corroborated the hypothesis by analyzing the somatic expression and copy number variations profile of over 11,000 pan-cancer tumor samples, and discussed their clinical impact based on survival outcomes. We showed that elevated PA levels lead to loss of DSP and promote migration in liver cancer (HepG2 and Hep3B) and breast cancer (MDA-MB-231) cell lines. Using a luciferase DSP-promoter assay and chromatin immunoprecipitation (ChIP)-qPCR, we validate that the transcriptional repression of DSP is mediated by the ZEB transcription factors downstream of the IRE1–XBP1 pathway. CRISPR-mediated knockout (KO) of IRE1 abrogated ZEB-mediated DSP repression and cell migration. Thus, our results demonstrate the mechanism of DSP loss in cancer cells driven by elevated PA uptake depends on the IRE1–XBP1–ZEB pathway.

HepG2, Hep3B, SNU-387, MCF10A, MDA-MB-231, and HCT116 cells were purchased from ATCC. Cell line authentication/certification was performed by ATCC. The cell lines were tested for Mycoplasma using a PCR-based method (ATCC's Universal Mycoplasma Detection Kit). HepG2, Hep3B, and MDA-MB-231 cells were cultured in DMEM supplemented with 10% FBS. SNU-387 cells were cultured in RPMI1640 medium supplemented with 10% FBS. HCT116 cells were cultured in McCoy's 5A medium supplemented with 10% FBS. MCF10A cells were cultured in DMEM/F12 medium supplemented with 5% horse serum, 0.5 μg/mL hydrocortisone, 100 ng/mL cholera toxin, 10 μg/mL insulin, and 20 ng/mL epidermal growth factor. All cell lines were maintained in a humid incubator with 5% CO₂ at 37°C and were used between P3-P15 passage numbers.

For DSP knockdown, Hep3B and HepG2 cells were seeded at a density of 5 × 10⁵ cells/mL and transfected with DSP siRNAs (Thermo Fisher Scientific, cat # s4333, s4335) or scramble siRNA (control, Thermo Fisher Scientific, cat # AM4611) using Lipofectamine RNAiMAX (Thermo Fisher Scientific, cat # 13778030) following the manufacturer's protocol. A dose response experiment was performed to determine the concentration of siRNA required for >70% knockdown of DSP expression. Concentration of 100 and 80 nmol/L was used to knock down ZEB1 and ZEB2, respectively. These concentrations were used for all further experiments. For ZEB1/2 knockdown, MDA-MB-231 and Hep3B cells were seeded at a density of 5 × 10⁵ cells/mL and transfected with ZEB1/2 siRNAs (ORIGENE, cat # SR304746A-C for ZEB1, Thermo Fisher Scientific, cat # AM16708 for ZEB2) or scramble siRNA (control: ORIGENE cat # SR30004 for ZEB1, Thermo Fisher Scientific, cat # AM4611 for ZEB2) using Lipofectamine RNAiMAX following the manufacturer's protocol. For Western blot analysis, 48 or 72 hours of RNAiMAX transfections were performed. For qRT-PCR, 24 hours of RNAiMAX transfections were performed.

The pSpCas9(BB)-2A-GFP (PX458) and pSpCas9(BB)-2A-Puro (PX459) V2.0 plasmids were a gift from Dr. Feng Zhang (Addgene plasmid # 48138 and # 62988). The pX333 plasmid was a gift from Dr. Andrea Ventura (Addgene plasmid # 64073). To target the Cas9 enzyme to the IRE1 gene, we designed sgRNAs using several CRISPR design servers, for example, crispr.mit.edu and crispr.dbcls.jp, and identified two sgRNAs (gRNA-A and gRNA-B) with high scores on exon 1, which were evaluated by COSMID and Cas OFFinder for off-target effects. In addition, we also identified a sgRNA on exon 17 that target the kinase domain of IRE1 (gRNA-C) with high score and verified by COSMID, to further ensure KO of IRE1 function. Three high-scoring single guide RNAs (sgRNA) were synthesized and inserted into the plasmids. gRNA-A (TCACCGCCTCGCTGTCGTCGCGG) and gRNA-B (CCGTACCGCCCCCGGAGCCAGGG) is inserted in the pX333 plasmid, while gRNA-C (CCAGAGGGAGGCCGCCGAATGGG) was inserted in the pSpCas9(BB)-2A-Puro (PX459) V2.0 plasmid. Subsequently, gRNA-A and gRNA-C were inserted within the Bbs1 cut sites and gRNA-B was inserted between the AflII and KpnI sites. All inserted constructs were confirmed by DNA sequencing. The plasmids were transfected into MDA-MD-231, Hep3B, and HepG2 cells using Lipofectamine 3000 (Thermo Fisher Scientific, cat # L3000001). After using the appropriate selection agents, GFP and puromycin, individual clonal populations were expanded and analyzed for loss of IRE1 expression using Western blotting. Clones with no detectable IRE1 were selected for further studies.

Transwell (Boyden chamber) migration assays were performed using cell culture inserts (8-μm pore size; Sigma-Aldrich, cat # CLS3464 and CELLTREAT #230639). A total of 5 × 10⁵ cells in serum-free media supplemented with 1 μg/mL mitomycin C (Sigma-Aldrich, cat # M4287) were seeded in the upper chamber of the inserts. To assess migration upon DSP silencing, lipofectamine RNAiMAX (Thermo Fisher Scientific, cat #13778030) was used for transfection following the manufacturer's protocol. After 24 hours of transfection at 37°C, the media was replaced with serum-free media supplemented with 1 μg/mL mitomycin C. The inserts were placed in wells with 500 μL of regular medium containing 10% FBS as chemoattractant. To assess migration with PA treatment, cells were seeded with 200 μL of serum-free media supplemented with 1 μg/mL mitomycin C in the upper chamber of the insert for 24 hours. The media in the insert was replaced with 200 μL PA media without FBS but contains 1 μg/mL mitomycin C. The insert was then placed in an outer well (using a 24-well plate) containing either BSA or PA media with 10% FBS as a chemoattractant for 24 hours. After 24 hours of incubation at 37°C, the inserts (of the DSP silencing or PA treatment) were removed, washed with PBS, and fixed in 70% ethanol for 10 minutes. The inserts were then dried for 15 minutes, stained with 0.2% (w/v) crystal violet (Sigma Aldrich, cat# C0775) in methanol for 15 minutes, and washed with PBS twice. After 15 minutes of drying, the number of cells that migrated across the membrane were counted across 3 fields of view using a fluorescence microscope at ×10 or ×20 magnification. Each migration assay was repeated three times.

Total mRNA was isolated from Hep3B, HepG2, and MDA-MB-231 wild-type (WT) and IRE1^−/− KO cells. A one-step RT-PCR Kit was used to amplify XBP1 cDNA (Qiagen, cat # 210210). XBP1-for 5′-TTACGAGAGAAAACTCATGGCC-3′ and XBP1-rev- 5′- GGGTCCAAGTTGTCCAGAATGC-3′ primers were designed to flank the 26 bp intron sequence and the products were resolved on a 2.5% agarose gel made in 1× Tris/Borate/EDTA (TBE).

Total mRNA was isolated from MDA-MB-231, Hep3B, and HepG2 WT and IRE1^−/− KO cells with an RNeasy Plus mRNA Extraction Kit (Qiagen cat # 74134). cDNA was prepared (Thermo Fisher Scientific, cat # 4368814) and equal amounts (100 ng) used for qRT-PCR with the iQ SyBr Green supermix (Bio-Rad, cat # 1708882) using the following parameters for 40 cycles: 95°C for 30 seconds, 58°C for 30 seconds, and 72°C for 45 seconds. The primer sequences for ZEB1 were 5′-ATGCAGCTGACTGTGAAGGT-3′ (forward) and 5′-GAAAATGCATCTGGTGTTCC-3′ (reverse), ZEB2 were 5′-TATGGCCTACACCTACCCAAC-3′ (forward) and 5′-AGGCCTGACATGTAGTCTTGTG-3′ (reverse), for DSP were 5′-TGTACGACTGGAGCGACAAG-3′ (forward) and 5′-GCGTCTGCAGAGTGTCCATA-3′ (reverse), and the housekeeping gene GAPDH were 5′- CAGCCGCATCTTCTTTTGCG-3′ (forward) and 5′- TGGAATTTGCCATGGGTGGA-3′ (reverse).

MDA-MB-231, Hep3B, and HepG2 cell lysates were collected using RIPA buffer containing a protease inhibitor cocktail (Roche, cat # 4693159001). Thirty to 50 μg total protein was loaded onto an SDS-PAGE gel run in a Tris/glycine/SDS buffer system. The proteins were transferred onto a nitrocellulose membrane and blocked with either 5% BSA or 5% nonfat dried milk for 3 hours or 1 hour, respectively, at room temperature. Primary antibody incubation was done overnight at 4°C with antibodies against IRE1 (Cell Signaling Technology, cat # 3294), DSP I + II (Abcam, cat # ab71690), E-cadherin (Cell Signaling Technology, cat # 3195), Vimentin (Cell Signaling Technology, cat # 3932), ZEB1 (ORIGENE, cat # TA802298), ZEB2 (ORIGENE, cat # TA802113), and GAPDH (GeneTex, cat # GTX100118).

The LightSwitch Luciferase Assay system (Active Motif) was used to insert the DSP promoter sequence upstream of an engineered luciferase gene (RenSP). A 96-well plate was seeded with 5,000 cells/well. A total of 150 ng of reporter plasmid containing the DSP promoter was then transfected into Hep3B, HepG2 cells, and MDA-MB-231 cells. siRNA designed against ZEB1, ZEB2, or a scrambled sequence was cotransfected along with the plasmid at a concentration of 200 nmol/L per well. The luciferase assay was carried out according to the LightSwitch Luciferase Assay Kit instructions after 24 hours (Active Motif, cat # 32031).

Human cancer cells (Hep3B, MDA-MB-231, HepG2) were fixed with 1% formaldehyde for 15 minutes and quenched with 0.125 mol/L glycine. Chromatin was isolated by the addition of lysis buffer, followed by disruption with a Dounce homogenizer. Lysates were sonicated and the DNA sheared to an average length of 300 to 500 bp. Genomic DNA (input) was prepared by treating aliquots of chromatin with RNase, proteinase K, and heat for de-crosslinking, followed by ethanol precipitation. Pellets were resuspended and the resulting DNA was quantified on a ClarioStar spectrophotometer. Extrapolation to the original chromatin volume allowed quantitation of the total chromatin yield.

An aliquot of chromatin (30 μg) was precleared with protein G agarose beads (Invitrogen). Genomic DNA regions of interest were isolated using 4 μg of antibody against ZEB1 (Santa Cruz Biotechnology, cat # sc-10572, lot # K0702). Complexes were washed, eluted from the beads with SDS buffer, and subjected to RNase and proteinase K treatment. Crosslinks were reversed by incubation overnight at 65°C, and ChIP DNA was purified by phenol–chloroform extraction and ethanol precipitation.

qPCR reactions were carried out in triplicate on specific genomic regions using SYBR Green Supermix (Bio-Rad, cat # 170-8882) on a CFX Connect Real Time PCR system. The qPCR was performed using one positive control primer pair for CDH1 5′-GTGAACCCTCAGCCAATCAG-3′ (forward) and 5′-TCACAGGTGCTTTGCAGTTC-3′ (reverse), a negative control primer pair that amplifies a region in a gene desert on chromosome 12 (Untr12; Active Motif Cat# 71001) and primers for four regions of interest along the DSP promoter region containing putative ZEB1/2 binding sites: three putative ZEB binding regions upstream of DSP transcription start site (−1 k, −462, ± 500 p from TSS) and one region located in the first intron. These regions were selected based on promoter analysis for putative binding sites and ZEB peaks observed in the ENCODE project ChIP-seq data. Primer sequences for target sequenes were: DSP -1k 5′-TTTTCATGGATGGTGTGACTG-3′ (forward), 5′-GGGCCACTCCCAGTTAAGAG-3′ (reverse); DSP -462 5′-CCCCAAACAAAACTCTATCATC-3′ (forward), 5′-GCTACCTACCACGCATTGAC-3′ (reverse); DSP Pro 5′-CTAAAGCGTTCCCTCCTCAC-3′ (forward), 5′-CGATTGGAGACTTACTGGTCAA-3′ (reverse); DSP In1 5′-GCCTCCGCAAGTAAGAGCTA-3′ (forward), 5′-GGAGCTTCTGCCTCTGTGAC-3′. The resulting signals were normalized for primer efficiency by carrying out qPCR for each primer pair using input DNA from each cell line.

Confocal microscopy with HepG2 cells transfected with DSP siRNA was performed as described previously (7). HepG2 cells were fixed 48 hours after siRNA transfection and incubated overnight with primary antibodies against DSP, JUP, and CDH1. The cells were then washed and incubated with AlexaFluor 488 secondary antibody, followed by nuclear counterstaining with DAPI. Images were acquired at ×40 magnification using an Olympus FluoView 1000 inverted microscope with identical exposure and PMT settings for each image.

Promoter sequence (1,000 bp upstream of transcription start site) for the human DSP gene was retrieved from the UCSC genome browser (hg38) and transcription factor binding analysis for putative ZEB binding sites was performed using the JASPAR gene regulatory database (29). Gene expression, methylation, copy number variation (CNV), and survival data for The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) cohorts were retrieved from the genomics data commons hub at the UCSC Xena portal for cancer genomics (30). The RNA sequencing gene expression profiles (FPKM) were upper-quantile normalized and standardized (mean = 0, SD = 1) prior to further analysis. The mean beta values of the DSP gene were obtained from Illumina methylation 450 array and log₂ transformed. For ERN1 CNV analysis, masked copy number segmentation values from the GISTIC2 pipeline were averaged for the ERN1 gene. Kaplan–Meier survival analyses were performed by segregating the TCGA and ICGC samples into two groups. The samples were clustered based on standardized expression or average masked copy number segmentation values using K-means clustering (k = 2) yielding groups with high or low average expression or CNV represented as cluster centers.

Statistical analyses for differences in mean were performed using Student t test in GraphPad Prism 7 with P < 0.05 considered significant. Kaplan–Meier survival analysis, linear regression, and correlation analyses were performed in R Studio (R version 3.5.1) with P < 0.05 considered statistically significant. Statistical analysis for differences in mean in experiments with PA and BSA treated WT and IRE1^−/− cells were performed using two-way ANOVA with P < 0.05 considered statistically significant.

To evaluate the impact of PA on DSP expression and migration in cancer cells, we compared the Hep3B liver cancer and MDA-MB-231 breast cancer cell lines treated with 0.3 mmol/L PA or 2% BSA (control). In each cell line, we observed a significant reduction in DSP expression by 24 to 48 hours posttreatment (Fig. 1A and B). Next, we determined whether the reduction in DSP expression in PA-treated cells depended on the IRE1–XBP1 pathway. Using CRISPR gene editing, we generate IRE1^−/− KO HepG2, Hep3B, and MDA-MB-231 cell lines (Supplementary Figs. S1A and S2–S4). Although PA treatment resulted in the accumulation of XBP1 splice product (XBP1s) in the parental lines, indicating activation of the IRE1–XBP1 pathway, the IRE1^−/− cells did not accumulate XBP1s upon PA treatment (Supplementary Fig. S1B). In the IRE^−/− cells, PA treatment did not result in a decrease in DSP expression levels, as compared with WT cells (Fig. 1A and B).

Next, we evaluated the impact of DSP knockdown and PA treatment on rates of migration in Hep3B, HepG2, and MDA-MB-231 cells. As expected, the rates of migration were elevated in cells with siRNA-mediated DSP knockdown (Fig. 1C–E; Supplementary Figs. S5A and S6). In comparison, cells treated with PA exhibited higher migration rates comparable with the rates observed in the cells with DSP knockdown (Fig. 1F–H; Supplementary Figs. S5B and S7). However, this increase in migration rates was not observed in IRE1^−/− cells, suggesting the activation of the IRE1–XBP1 pathway was critical in mediating the effects of PA on cell migration (Fig. 1F–H).

Previous studies have linked loss of cell adhesion molecules like DSP with the activation of EMT program, a complex set of cellular alterations that drive multiple metastatic processes including cell migration. To determine whether loss of DSP triggered EMT activation or was sufficient in driving cancer cell migration on its own, we assessed the impact of loss of DSP expression on the established EMT markers, including E-cadherin (CDH1) and vimentin (VIM). Although cells transfected with DSP siRNA showed a decrease in DSP expression (Fig. 2A and B), neither E-cadherin (Fig. 2C) or vimentin (Fig. 2D) were impacted by the loss of DSP expression in Hep3B, HepG2, and MDA-MB-231 cells. Next, we assessed the expression and localization of the desmosomal components DSP and junction plakoglobin (JUP) in HepG2 cells compared with E-cadherin. Upon siRNA-mediated DSP knockdown, we observed both a decrease in DSP and JUP expression, as well loss of localization at the cell membrane (Fig. 2E). This suggested DSP knockdown resulted in loss of desmosomes. However, neither the expression nor the cell membrane localization of E-cadherin was impacted by DSP knockdown (Fig. 2E). Taken together with earlier results showing enhanced migration in cells with DSP knockdown, these results suggest that the loss of DSP itself may be sufficient to promote cell migration.

Considering recent evidence linking XBP1 with transcriptional activation of the ZEB family of transcriptional repressors (28), we analyzed the promoter sequence of DSP and found three putative ZEB binding sites (Fig. 3A). To corroborate the hypothesis that ZEB transcription factors function as transcriptional repressors of DSP, we analyzed gene expression data from the TCGA PANCAN cohort. Correlation analysis revealed that ZEB1 and ZEB2 showed strong negative correlation with DSP expression (both P < 2.2 × 10⁻¹⁶; Fig. 3B). These correlative patterns were not just limited to liver or breast cancer, but consistent across different cancer types (Supplementary Fig. S8). In addition, average DSP methylation levels were negatively correlated with DSP expression while being positively correlated with ZEB1 and ZEB2 expression (Fig. 3C). We evaluated the correlation between DSP methylation levels and the expression of DSP, ZEB1, and ZEB2 across liver, breast, and colorectal cancer tumors and found consistent patterns within these individual cancer types (Supplementary Fig. S9). These results suggest that ZEB transcription factors may be responsible for transcriptional repression of DSP.

We next measured the basal expression levels of DSP, ZEB1, and ZEB2 in six cancer cell lines to validate the correlation patterns observed in the TCGA analysis. Similar to the human tumor data, we found a strong negative correlation between DSP and ZEB expression across the six cell lines (Fig. 4A). Because MDA-MB-231 cells have the highest endogenous levels of ZEB and lowest endogenous levels of DSP, we knocked down ZEB1/2 expression using siRNAs and found that DSP levels increased significantly (Supplementary Fig. S10). Likewise, we found similar results with the Hep3B cells (Supplementary Fig. S11). To functionally validate the role of ZEB in the regulation of DSP expression, we cloned the DSP promoter sequence in a luciferase reporter gene with the three putative ZEB binding sites upstream of the transcription start site. Upon cotransfecting Hep3B and MDA-MB-231 cells with the DSP-promoter luciferase plasmid and siRNAs targeting ZEB1 or ZEB2, we found that DSP promoter activity was significantly enhanced upon knockdown of ZEB1 and ZEB2 (Fig. 4B and C). In addition, for further validation, we performed ChIP-qPCR to detect enrichment of ZEB1 binding on various predicted sites along the DSP promoter (Fig. 4D–F; Supplementary Fig. S12). In each cell line, we observed a significantly greater number of ZEB1 binding events at different putative binding sites as compared with negative control.

Next, we investigated the direct impact of the IRE1–XBP1 pathway in the activation of ZEB transcription factors in the presence of elevated PA. We observed a significant increase in the expression levels of ZEB1 upon PA treatment in WT Hep3B and MDA-MB-231 cells (Fig. 4G and H). However, the ZEB1 expression was significantly lower in the PA-treated IRE^−/− cells, suggesting ZEB expression was dependent on the IRE1–XBP1 pathway (Fig. 4G and H; Supplementary Figs. S13 and S14).

The in vitro experiments confirmed that DSP loss via PA depended on the activation of the IRE1–XBP1–ZEB pathway and led to enhanced migration of cancer cells. As increased migration rates are often considered a hallmark of advanced malignancy, we explored the clinical relevance of somatic alterations of DSP and IRE1 in patients with cancer. We first analyzed the relative impact of DSP expression on patient survival outcomes in the TCGA PANCAN and ICGC cohorts. We grouped tumor samples from the two cohorts using K-means clustering, and determined the impact of samples clustered on the basis of low or high expression of survival outcomes. As shown in Fig. 5A, the group of samples with low DSP expression were associated with worse overall survival outcomes compared with high DSP expression. In addition, we compared DSP expression in TCGA samples based on pathologic assessment of metastatic status (where available). In this analysis, DSP expression levels were significantly lower in tumors with evidence of distant metastasis (M1) compared with tumors without any metastases (M0; Fig. 5B). Next, we evaluated the impact of IRE1 (ERN1) on survival outcomes. Although IRE1 expression levels did not impact survival outcomes, we found increased copy number of the ERN1 gene were associated with poor overall survival outcomes in both the TCGA and ICGC cohorts (Fig. 5C). Although preliminary, this evidence suggests both loss of DSP and gain of IRE1/ERN1 may be important factors in determining worse patient prognosis, demanding further investigations.

Along with the intrinsic cellular factors that drive transformation to advanced metastatic state, the tumor microenvironment is increasingly being recognized as a crucial mediator in the survival and growth of cancer cells (31, 32). We now recognize fatty acid metabolism plays an important role in cancer progression and response to therapeutic intervention (33, 34). Here, we report that elevated PA levels can promote migration of cancer cells by depleting desmoplakin expression. Owing to the critical role of desmoplakin in the structural integrity of desmosomes, previous studies have speculated a tumor suppressor role of desmoplakin in cancers (10, 13). These reports highlighted loss of desmosomal adhesion as prerequisite for the induction of EMT program, along with the unidirectional dependency of desmosome formation on adherence junctions (35). However, our results suggest that loss of desmoplakin itself is sufficient to enhance migration of cancer cells. Interestingly, this effect seems to be independent of the loss of E-cadherin or the induction of the classic EMT program. Thus, our study suggests an important and independent mechanism of cancer cell migration that depends on the loss of desmoplakin.

At the cellular level, elevated fatty acid levels have been reported to influence various signaling pathways, including ER stress and the UPR. The activation of IRE1–XBP1 axis in the UPR is a well-established consequence of elevated PA levels in cancer cells (36). In our study, we show that the loss of desmoplakin actually depends on the activation of the IRE1–XBP1 pathway. In the presence of PA, cancer cell lines undergo loss of desmoplakin and enhanced migration. However, these effects of elevated PA levels were abrogated in the IRE^−/− KO cells. Thus, our results provide direct evidence linking the IRE1–XBP1 pathway with loss of DSP and enhanced cancer cell migration in the presence of PA.

We further explored the precise mechanism by which IRE–XBP1 pathway could regulate DSP expression. On the basis of systematic analysis of the DSP promoter, we identified multiple ZEB binding sites that could suggest a transcriptional repression mechanism triggered by IRE1–XBP1. This hypothesis derives from a recent study suggesting the spliced and active form of XBP1 (XBP1s) is a direct transcriptional activator of SNAI1, SNAI2, ZEB2, and TCF3 transcription factors (28). We corroborated the correlative expression patterns between DSP expression and methylation levels with ZEB expression across a large cohort of human tumor samples from the TCGA and ICGC project. Moreover, we demonstrated a direct link between ZEB expression and DSP promoter activity, thereby confirming the regulatory link between ZEB and DSP. In addition, we demonstrate that ZEB expression levels were elevated in cells treated with PA, while this effect was suppressed in the IRE^−/− KO cells. On the basis of these results, we confirmed our hypotheses of DSP regulation via the IRE1–XBP1–ZEB axis and proposed the functional model in Fig. 5D.

As we continue to gain further evidence supporting the molecular mechanisms that govern cancer cell progression in the presence of elevated PA, we must also scrutinize the phenotypic impact of these alterations in patient tumors. We investigated the relevance of loss of DSP expression and gain of IRE1 genomic copy number in patient prognosis. Our analyses revealed a significant impact of both alterations on poor patient outcomes, as would be expected based on enhanced migration of the cancer cells observed in our experiments. Although these are preliminary and speculative connections, they highlight the need to study the role of these pathways in further details.

No disclosures were reported.

A. Nath: Conceptualization, formal analysis, investigation, methodology, writing–original draft, writing–review and editing. A. Oak: Formal analysis, investigation, methodology, writing–review and editing. K.Y. Chen: Formal analysis, investigation, methodology, writing–review and editing. I. Li: Formal analysis, investigation, methodology, writing–review and editing. R.C. Splichal: Investigation, methodology, writing–review and editing. J. Portis: Investigation, methodology, writing–review and editing. S. Foster: Investigation, methodology, writing–review and editing. S.P. Walton: Investigation, methodology, writing–review and editing. C. Chan: Conceptualization, resources, formal analysis, supervision, funding acquisition, investigation, methodology, writing–review and editing.

The authors would like to thank Dr. Melinda Frame at the Center for Advanced Microscopy at Michigan State University for assistance with the confocal microscopy experiments. The authors are also grateful for the excellent service and support received from Maddy Craske's team at Active Motif for the ChIP-qPCR experiments. This work was supported in part by the National Science Foundation (CBET 1547518, 1510895, 1802992, 2029319) and the NIH (R21NS116496).

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.