MITF, TFEB, and TFE3 drive distinct adaptive gene expression programs and immune infiltration in melanoma

Diogo Dias; Erica Oliveira; Román Martí-Díaz; Sarah Andrews; Ana Chocarro-Calvo; Alice Bellini; Laura Mosteo; Yurena Vivas García; Jagat Chauhan; Linxin Li; José Manuel García-Martinez; José Neptuno Rodriguez-López; Silvya Stuchi Maria-Engler; Colin Kenny; Javier Martínez-Useros; Custodia García-Jiménez; Luis Sanchez-del-Campo; Pakavarin Louphrasitthiphol; Colin R Goding

doi:10.1016/j.celrep.2025.116499

. Author manuscript; available in PMC: 2026 Jan 24.

Published in final edited form as: Cell Rep. 2025 Nov 22;44(12):116499. doi: 10.1016/j.celrep.2025.116499

MITF, TFEB, and TFE3 drive distinct adaptive gene expression programs and immune infiltration in melanoma

Diogo Dias ¹, Erica Oliveira ¹, Román Martí-Díaz ², Sarah Andrews ¹, Ana Chocarro-Calvo ³, Alice Bellini ¹, Laura Mosteo ¹, Yurena Vivas García ¹, Jagat Chauhan ¹, Linxin Li ¹, José Manuel García-Martinez ³, José Neptuno Rodriguez-López ², Silvya Stuchi Maria-Engler ⁴, Colin Kenny ^5,⁶, Javier Martínez-Useros ³, Custodia García-Jiménez ³, Luis Sanchez-del-Campo ², Pakavarin Louphrasitthiphol ^1,^*, Colin R Goding ^1,^7,^*

PMCID: PMC12828906 NIHMSID: NIHMS2132725 PMID: 41275493

SUMMARY

Cells can contain multiple related transcription factors targeting the same sequences, leading to potential regulatory cooperativity, redundancy, competition, or temporally regulated factor exchange. Yet, the differential biological functions of co-targeting transcription factors are poorly understood. In melanoma, three highly related transcription factors are co-expressed: the mammalian target of rapamycin complex 1 (mTORC1)-regulated TFEB and TFE3 (both key effectors of a wide range of metabolic and microenvironmental cues assumed to perform similar functions) and the microphthalmia-associated transcription factor (MITF), which controls melanoma phenotypic identity. Here, we reveal the functional specialization of MITF, TFE3, and TFEB and their impact on melanoma progression. Notably, although all bind the same sequences, each regulates different and frequently opposing gene expression programs to coordinate differentiation, metabolism, and protein synthesis and qualitatively and quantitatively impacts tumor immune infiltration. The results uncover a hierarchical cascade whereby microenvironmental stresses, including glucose limitation, lead MITF, TFEB, and TFE3 to drive distinct biologically important transcription programs that underpin phenotypic transitions in cancer.

In brief

Dias et al. reveal that in melanoma, the transcription factors MITF, TFE3, and TFEB share common binding sites but impose distinct transcriptional programs. Stresses promoting an MITF-to-TFEB-to-TFE3 switch will lead cells to suppress proliferation, reprogram metabolism, and impact tumor immune cell infiltration quantitatively and qualitatively.

Graphical Abstract

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INTRODUCTION

Co-expression of structurally related transcription factors in cells can lead to potential regulatory cooperativity, redundancy, competition, or sequential activity at the same regulatory elements.¹ This may allow flexibility in controlling common target genes, especially if the co-targeting transcription factors perform non-equivalent functions. However, our understanding of how functionally similar transcription factors perform distinct roles is limited. This is particularly important in cancer, where disease progression is largely driven by microenvironment-mediated changes in cell phenotype.^2–4 For example, nutrient or oxygen limitation leads cells to employ pro-survival strategies, including metabolic reprogramming and migrating to seek an improved environment.^5,6 Having multiple, highly related transcription factors targeting the same genes but eliciting different outcomes may be a key mechanism that allows cells to undergo appropriate non-genetic adaptations, such as a switch from proliferation to invasion.

Melanoma represents an excellent model to understand the complex interplay between transcription factors, cell plasticity, the microenvironment, and disease progression.⁷ The microphthalmia-associated transcription factor (MITF),⁸ a lineage survival oncogene,⁹ is a key determinant of phenotypic identity and plays a critical coordinating role in many aspects of melanoma biology.¹⁰ MITF promotes survival,^9,11 proliferation,^12,13 and differentiation^14,15; regulates metabolism^16–19; controls DNA damage repair^20,21; and suppresses senescence²² and invasion.²³ In vivo, MITF expression is dynamic²⁴ and is suppressed by several microenvironmental stresses,¹⁰ including nutrient limitation,^25,26 hypoxia,^19,27 and inflammatory signaling,²⁵ that slow proliferation but facilitate metastatic dissemination. However, while MITF expression is reduced in invasive cells²³ that have elevated tumor-initiating ability^25,28 and exhibit non-genetic therapy resistance,^29–32 for invasive cells to generate a new metastasis, MITF should be re-expressed to allow proliferation.^15,33 Despite these insights, our understanding of the molecular adaptations occurring as cells are subject to potentially lethal stresses during disease progression remains incomplete.

One possibility, yet to be fully explored, is that upon the downregulation of MITF, the MITF-related transcription factors EB and E3 (TFEB and TFE3)^10,34,35 take on MITF functions. TFE3 and TFEB are key regulators of autophagy and lysosome biogenesis^34,36–38 but are also implicated in many other cellular processes, for example, modulating the integrated stress response,^39,40 insulin expression,⁴¹ the innate immune response,^42,43 recovery from exercise,⁴⁴ and lineage specification and stem cell fate.^45,46 Not surprisingly, given their importance, deregulation of TFEB and TFE3 is implicated in cancer.^47–51 Unlike the melanocyte/melanoma-specific isoform of MITF, in nutrient-rich conditions, TFE3 and TFEB are retained in the cytoplasm via 14–3-3 protein interaction⁵² following their phosphorylation by the mammalian target of rapamycin complex 1 (mTORC1).^34,53 mTORC1-mediated phosphorylation of TFEB and TFE3 requires their recruitment to endolysosomal membranes by the RAGC/D GTPases,⁵⁴ where their phosphorylation is dependent on folliculin (FLCN), a RAGC activator.⁵⁵ Upon nutrient limitation, decreased mTORC1 activity and dephosphorylation^56,57 permit TFEB and TFE3 to enter the nucleus to regulate transcription.

Here, we investigate the role of TFEB and TFE3 in cells that also express MITF to better understand the potential of these related transcription factors to exhibit distinct and non-redundant roles in gene expression and melanoma progression.

RESULTS

MITF promotes mTORC1 signaling

If starvation can trigger invasion, why would MITF^Low cells maintain invasiveness under nutrient-rich culture conditions? We hypothesized that MITF^Low cells might fail to sense or use nutrients to impact the regulation of TFEB and TFE3. We therefore examined the effect of MITF depletion on mTORC1 that promotes protein translation by increasing S6 kinase-mediated activation of S6 and phosphorylation of 4EBP1.⁵⁸ Depletion of MITF in MITF^High IGR37 cells led to lower phosphorylation of S6 and 4EBP1 (Figure 1A). Consistent with previous results,⁵⁹ silencing MITF led to an increase in PTEN protein (Figure 1A, left) and mRNA (Figure 1A, right), an inhibitor of PI3 kinase upstream of mTORC1.

Figure 1. — (A) Left, western blot of IGR37 melanoma cell line transfected with control or MITF-specific small interfering RNA (siRNA). Right, RT-qPCR for *PTEN* mRNA from corresponding IGR37 cells.

(B and C) Heatmaps showing relative mRNA expression in CCLE melanoma or Tsoi et al.⁶³ melanoma cell lines. (B) Right, Pearson correlation of gene expression.

(D) TCGA melanomas ranked by MITF expression (*MITF*; black line). Gray bars indicate expression of *RRAGD* or *FNIP2* in each melanoma, with the moving average of each per 20 melanoma window indicated by colored lines.

(E) MITF ChIP-seq showing binding to the *RRAGD* or *FNIP2* genes.

(F) Western blot of melanoma cell lines.

(G and H) Box and whisker plots showing relative expression of *MITF*, *RRAGD*, and *FNIP2* based on triplicate RNA-seq of IGR37 cells expressing doxycycline-inducible β-catenin (iCTNNB1) (G) or in MeWo cells after short hairpin RNA (shRNA)-mediated depletion of MITF (H).

See also Figure S1.

To identify PTEN-independent mTORC1 inhibitory mechanisms, we examined the RAG GTPases and FLCN-interacting protein 2 (FNIP2) that modulate mTORC1 signaling at the lysosome^55,60–62 to control TFEB and TFE3. In the Cancer Cell Line Encyclopedia (CCLE), expression of FNIP2 and RRAGD, and to some extent RRAGC, but not RRAGA or RRAGB, correlates with MITF expression (Figure 1B). The correlation was reproduced in the 53 melanoma cell lines described by Tsoi et al.⁶³ that cluster into 4 distinct phenotypes (Figure 1C). RRAGD and FNIP2 mRNA levels were strongly expressed in the MITF^High transitory and melanocytic phenotypes but not in the undifferentiated or neural crest-like phenotypes. The correlation between MITF and RRAGD and FNIP2 mRNA expression was reproduced using The Cancer Genome Atlas (TCGA) melanoma cohort (Figure 1D). Importantly, chromatin immunoprecipitation sequencing (ChIP-seq)⁶⁴ revealed MITF binding to the promoters of RRAGD and isoform 2 of FNIP2 (Figure 1E) and FNIP2, and RRAGD expression was lower in the MITF^Low WM793 and IGR39 melanoma cell lines compared to MITF^High IGR37 and 501mel cells (Figure 1F). Doxycycline (Dox)-mediated induction of β-catenin (CTNNB1), an MITF co-factor⁶⁵ and activator of MITF expression,⁶⁶ in IGR37 cells induced FNIP2 and RRAGD expression (Figure 1G), while depletion of MITF decreased their expression (Figure 1H). These data suggest that FNIP2 and RRAGD are MITF target genes and that signaling to TFEB and TFE3 would potentially be deregulated in MITF^Low melanoma cells.

Differential regulation of TFEB and TFE3 by glucose limitation

Consistent with previous observations in melanoma,⁶⁷ in TCGA melanoma cohort, TFE3 mRNA was inversely correlated with MITF (Figure 2A), while a moderate anti-correlation was also noted between MITF and TFEB (Figure 2B). By contrast, TFEB and TFE3 mRNA were correlated (Figure 2C). Using the Tsoi et al.⁶³ melanoma cell line panel (Figure 2D), TFE3 mRNA was primarily expressed in the MITF^Low undifferentiated and neural crest-like phenotypes, while TFEB mRNA expression was not phenotype restricted. In an additional panel of 12 cell lines, mRNA-seq indicated predominantly low TFE3 expression in MITF^High cell lines, while TFEB mRNA expression was variable (Figure 2E). Western blotting (Figure 2F) revealed that the TFEB protein was more abundant in MITF-expressing cells, with SKMEL28 and SKMEL30 being notable exceptions, but TFE3 expression was largely independent of MITF status. Thus, some cells express all three MITF family members, but in others, only TFE3 is expressed. Any discordance between the TFE3 or TFEB protein and mRNA expression may arise because, unlike TFEB, under nutrient-replete conditions, TFE3 is subject to CUL^β-TrCP-dependent ubiquitination and degradation,⁶⁸ and other factors, such as differential translation efficiency, may also play a role.

Immunofluorescence using the MITF^High (501mel and IGR37) and MITF^Low (IGR39 and WM793) cell lines revealed low nuclear expression of TFEB (Figure S1A), which was increased using Torin, an mTOR inhibitor. Similarly, TFE3 was also largely nuclear in the MITF^High 501mel and IGR37 cells, with expression being increased by Torin. However, unlike TFEB, in IGR39 cells, TFE3 was predominantly cytoplasmic but accumulated in the nucleus following Torin treatment. In WM793 cells, the low levels of cytoplasmic TFE3 exhibited a moderate nuclear accumulation in response to Torin.

Western blotting of fractionated extracts from 501mel cells revealed that Torin induced a loss of the slow-migrating phosphorylated form of TFEB and its nuclear accumulation (Figure S1B). Upon depletion of MITF, there was a similar accumulation of nuclear TFEB using Torin, but cytoplasmic phosphorylated TFEB levels were reduced. Notably, immunohistochemistry using tissue sections from three different human melanomas (melanomas 1–3) revealed a significant proportion of melanoma cells exhibiting nuclear staining of TFEB (Figure S1C), suggesting that, in vivo, mTORC1 signaling is suppressed. This is consistent with 63% of melanoma metastases exhibiting nuclear TFE3 staining.⁶⁹ While the high nuclear MITF in melanoma 2 correlated with the lowest total TFEB staining, because the same sections were not probed with both antibodies, the relationship between TFEB and MITF expression and subcellular localization in individual cells in vivo remains uncertain.

Glucose limitation, frequently found in tumors, regulates TFEB nuclear/cytoplasmic shuttling.⁷⁰ Consistent with previous work,²⁶ low glucose, silenced MITF (Figure 2G), and TFEB exhibited, at 4 h, a faster mobility characteristic of its dephosphorylation and nuclear accumulation.^55,70,71 However, by 24 h, TFEB expression was strongly suppressed (Figure 2G). By contrast, TFE3 expression was not silenced, and after 48 h in low glucose, it was the only MITF family member expressed. Similar results were obtained in HT29 colon cancer cells (Figure 2H), where glucose depletion downregulated TFEB but upregulated TFE3. We therefore asked whether depletion of TFE3 might affect the cell cycle or survival in glucose-deprived cells. The results (Figure 2I) revealed that in low glucose, depletion of TFE3 led to a dramatic increase in the sub-G1 population and a corresponding decrease in G1 cells, suggesting that TFE3 plays a key pro-survival role.

MITF, TFE3, and TFEB bind the same genes

Under glucose limitation, MITF and TFEB expression are suppressed, but TFE3 is maintained. Although all three proteins have an identical basic region that makes sequence-specific DNA contacts (Figure 3A), differential interaction with co-factors might enable them to differentially regulate subsets of genes. We therefore used 501mel human melanoma cells, in which we previously mapped genome-wide binding of MITF,^64,72 to express Dox-inducible hemagglutinin (HA)-tagged TFEB (Figure 3B) and performed ChIP-seq using the highly specific HA antibody. Since mTORC1 inhibits TFEB, we performed ChIP-seq using 0 or 5 ng of Dox ± 250 nM Torin, which induced nuclear accumulation of HA-tagged TFEB (Figure S2A). Replicate ChIPs showed a good concordance (Figure S2B). The results (Figure 3C) revealed substantial genome occupancy without Dox and Torin, consistent with TFEB cycling in and out of the nucleus under nutrient-rich conditions.^70,73 Nevertheless, Torin treatment increased the mean peak score and the number of binding sites recognized (Figure S2C). Using 5 ng Dox, HA-TFEB DNA binding increased to 9,576 sites (Figure S2D) with a higher mean peak score (Figure 3C), consistent with previous work using inducible MITF.^64,72 Using both Dox and Torin led to 39,582 sites being occupied (Figure S2D), of which 9,522 were bound in common with the Dox-only condition. These data were reflected in the read density maps (Figure S2E).

We next repeated the ChIP-seq using HA-TFE3 in the absence of Dox to maintain TFE3 at levels seen in parental cells (Figure 3D). Torin increased nuclear HA-TFE3 (Figure S2F) with good concordance between replicates (Figures S2G and S2H). We then compared the results with those of TFEB (Figure 3E) and also undertook a 3-way comparison with a previously published⁶⁴ MITF ChIP (Figure 3F). These analyses revealed 37,123 sites (Figure 3F, left) at 11,219 genes (Figure 3F, right) bound by all three factors but also many peaks apparently bound by each alone or by any two factors. For example, MITF and TFE3 share 32,649 peaks at 3,217 genes that are not called in the TFEB ChIP-seq.

We noted that the peak score of sites bound by all three factors was higher than that of sites bound by any single or combination of factors (Figure 3G). Thus, rather than being biologically relevant, peaks unique to each factor were more likely “bioinformatically unique.” Examination of binding at many loci, including those in Figure S2I, supported this notion. For example, at the ATAD3B gene (Figure S2I, left), an “MITF-unique” peak is also present in the TFE3 ChIP but was not called because of elevated reads in the TFE3 input control. It was not called in the TFEB ChIP, owing to reduced ChIP efficiency and higher reads in the input control. Similarly, a “TFE3-unique” peak upstream of the BLOC1S6 gene (Figure S2I, right) is present in the MITF ChIP but is not called, owing to high input control reads. At both loci, only a partial consensus MITF family binding site was found beneath the unique peaks. Using an alternative differential tag-density approach with the ChIP-seq data from the other two factors as an input, we did not detect peaks unique to any one factor (Figure S2J). Overall, while we cannot rule out binding by individual factors to a few unique genes, most potential regulatory sites are likely bound by all three factors.

All three factors recognized a similar 8 bp consensus TCACGTGA (Figure 3H) previously identified for MITF,⁷⁴ also known as the CLEAR box,³⁶ and exhibited a similar genomic distribution (Figure 3I). As examples, binding to four known MITF target genes is shown in Figure 3J.

To ask whether factor exchange occurs, we interrogated a recently published CUT&RUN dataset⁷⁵ obtained for MITF and TFE3 in SKMEL28 wild-type (WT) cells or a previously published⁷⁶ MITF knockout (KO). SKMEL28 cells do not express TFEB (Figure 2F), which simplifies data interpretation. The tag density heatmaps in Figure S2K reveal that the MITF KO exhibits greatly reduced MITF binding, with residual binding reflecting some antibody cross-reactivity with another MITF family member, as reported.⁷⁵ Importantly, the low TFE3 binding in the WT cells increased substantially in the MITF KO, likely owing to the absence of competition and because low MITF reduces mTORC1 activity (Figure 1), leading to elevated nuclear accumulation of TFE3. As an example, at DCT (Figure S2L) or TRPM1 (Figure S2M), there is reduced MITF binding in the MITF KO but increased TFE3 occupancy. By contrast, at the GPR143 locus, TFE3 binding occurs in neither the WT nor the MITF KO cells (Figure S2N). It is possible that loss of MITF leads to nucleosome remodeling, rendering sites at genes such as GPR143 inaccessible to TFE3.

MITF family members regulate different gene sets

Although MITF, TFEB, and TFE3 bind similar targets, DNA binding does not necessarily equate to regulation.^77,78 We therefore examined the contribution of each factor to gene expression using MITF^High 501mel human melanoma cells that express all three factors (Figure 2F). We initially performed RNA-seq on 501mel cells exposed to HEPES-buffered saline solution (HBSS) that contains glucose but lacks amino acids. The results revealed over 2,400 differentially expressed genes (DEGs) (fold change [FC] ≥ 2; p < 0.05) (Figures 4A and 4B; Table S1). Gene set enrichment analysis (GSEA) (Figures 4C and 4D) revealed downregulation of the HALLMARK E2F1 and MYC TARGET gene sets, as well as DNA repair genes previously reported to be targets of MITF.^20,21,79,80

We next used CRISPR-Cas9 to knock out each gene and were successful in inactivating TFEB and TFE3 and in making a TFE3/TFEB double KO (DKO) (Figure 4E) but were unable to generate an MITF KO. The TFEB KO cell lines exhibited modest upregulation of TFE3 but no change in MITF. The TFE3 KO cells moderately downregulate TFEB, especially clone 2, with some suppression of MITF. RNA-seq of TFEB (clone 2) and TFE3 (clone 1) KOs and the DKO grown under standard culture conditions revealed changes in gene expression (Figures 4F and 4G) consistent with TFEB and TFE3 possessing transcriptional activity under nutrient-replete conditions. Importantly, GSEA (Figure 4H) revealed that KO of TFE3 or TFEB had different effects on gene expression. The TFE3 KO exhibited enrichment of the E2F TARGETS and G2M CHECKPOINT gene sets and downregulation of gene sets including HYPOXIA, COMPLEMENT, interleukin (IL)-2/STAT SIGNALING, and APOPTOSIS (Figure 4H, highlighted in green). By contrast, in the TFEB KO, these gene sets were regulated in the opposite way. In addition, MTORC1 SIGNALING was downregulated in the TFE3 KO and the TFE3/TFEB DKO but not in the TFEB KO. Many gene sets displaying altered expression in the single KOs were not differentially expressed in the DKO, suggesting the effect of one KO was partly reversed by KO of the related gene.

Torin treatment of the parental cells or the three KO cell lines led to altered gene expression (Figures S3A and S3B), and, as expected in the parental cells, GSEA revealed significant downregulation by Torin of the mTORC1 SIGNALING, MYC, and E2F TARGET gene sets (Figure S3C) and a moderate enrichment of the epithelial to mesenchymal transition (EMT) gene set.

Remarkably, all DEG sets were downregulated in Torin-treated TFE3 KO cells, and none were significantly upregulated (Figure S3D). The TFE3 KO therefore appears to amplify the effect of Torin, leading to suppression of gene sets associated with mTORC1 SIGNALING and CHOLESTEROL HOMEOSTASIS, as well as E2F and MYC TARGETS. The enrichment of the EMT gene set in parental cells exposed to Torin was reversed in the Torin-treated TFE3 KO.

Using the TFEB KO cells (Figure S3E), we again obtained a result largely the opposite of that seen using the TFE3 KO. For example, enrichment of the MTORC1 SIGNALING gene set was reduced in the parental and TFE3 KO cells by Torin but was increased in TFEB KO cells. By contrast, the MYC TARGETS V1 gene set was decreased in the parental, TFE3, TFEB KO, and DKO cell lines on Torin treatment (Figure S3F).

Collectively, these data indicate that while both TFE3 and TFEB bind a similar repertoire of target genes, their effects on gene expression may be different. However, data interpretation is complicated by 501mel cells expressing MITF. We therefore used a previously described⁸¹ murine B16-F10 cell line bearing a frameshift mutation in the MITF gene (Figure 5A). Note that close inspection reveals a minor fast-migrating band cross-reacting with the anti-MITF antibody in the MITF KO, suggesting that they may express a low level of a truncated form of MITF, as has been observed previously⁷⁶ in an SKMEL28 MITF KO cell line. We nevertheless used CRISPR-Cas9 to knock out TFE3 and TFEB in the parental and MITF KO B16-F10 cells (Figure 5B).

Figure 5. — (A) Western blot using B16-F10 parental and functional *MITF* KO cell lines.

(B) Western blot using parental and indicated B16-F10 KO cell lines.

(C) Triplicate RNA-seq showing differential gene expression between parental and indicated B16-F10 KO cell lines.

(D–K) GSEA plots showing significantly differentially enriched gene sets comparing parental or indicated *MITF*, *TFEB*, and *TFE3* B16-F10 KO cell lines.

See also Figures S4 and S5 and Table S2.

RNA-seq of each cell line grown under standard culture conditions revealed robust remodeling of the transcriptional landscape (Figures S4A–S4G). Notably, compared to the MITF KO (Figure S4A), fewer genes were differentially expressed in the TFE3 and TFEB single KOs (Figures S4B and S4C). The MITF/TFEB DKO (Figures S4F) or the triple KO (TKO) (Figure S4G) also exhibited more DEGs, suggesting that some genes were regulated by more than one family member. Hierarchical clustering of the RNA-seq of the parental and KO cells revealed a complex gene expression pattern (Figure 5C), with differential expression of the genes in each cluster, labeled A–G, provided in Table S2. For simplification, we only discuss a subset of the more interesting features.

First, the KO of MITF has a bigger impact than the KO of either TFEB or TFE3 alone. For example, genes within clusters A3 and C2 are more upregulated in the MITF KO than in the TFE3 or TFEB KOs (Figure 5C, green). Similarly, many genes downregulated in the MITF KO in clusters B and D are unaffected or increased in the TFEB or TFE3 single KOs. The limited effect of the TFE3 and TFEB single KOs may reflect that gene expression was measured in cells in a nutrient-rich medium where the TFEB/TFE3 inhibitor mTORC1 is active. Second, the upregulation of MITF-repressed genes in clusters A3 and C2 is reversed in the MITF/TFEB DKO or the TKO, indicating that TFEB upregulates these genes in the absence of MITF. Third, cluster B (Figure 5C, brown) contains genes moderately downregulated in the MITF KO but strongly downregulated in the MITF/TFEB DKO and the TKO. Thus, MITF and TFEB, in the absence of MITF, positively regulate this gene set. Fourth, genes within clusters A1 and C1 (Figure 5C, blue) were upregulated in the MITF/TFE3 DKO compared to the MITF KO, with the effect of the DKO being reversed in the TKO. MITF and TFE3 therefore prevent upregulation of these genes by TFEB. Fifth, cluster G genes (Figure 5C, pink) were upregulated in the MITF/TFEB DKO but not in the TKO. These are therefore controlled by TFE3 in the absence of both MITF and TFEB, as occurs when under glucose depletion. Finally, cluster D genes (Figure 5C, red) downregulated in the MITF KO are largely unchanged in the TFEB, TFE3, or TFEB/TFE3 KOs. These genes appear to be MITF dependent but independent of TFEB and TFE3. Overall, each member of the MITF transcription factor family may compensate for another at some genes, but elsewhere, they may play opposing roles.

GSEA revealed MYC TARGETS, E2F TARGETS, and G2M CHECKPOINT gene set downregulation in the MITF KO (Figure 5D) but enrichment in the TFE3 KO (Figure 5E), indicating opposing regulation of these pro-proliferative gene sets by MITF and TFE3. The MYC and E2F TARGETS gene sets were unchanged in the TFEB KO (Figure 5F).

The MTORC1 SIGNALING gene set was enriched in the TFEB KO and TFE3/TFEB DKO (Figure 5F) but suppressed in the MITF/TFEB DKO (Figure 5I), the MITF/TFE3 DKO (Figure 5H), and the TKO (Figure 5J). This indicates that the enrichment in the TFEB KO was dependent on MITF and, to some extent, on TFE3.

The GLYCOLYSIS gene set was enriched in the TFEB KO (Figure 5F) and TFEB/TFE3 DKO (Figure 5G) but not in the TKO. No enrichment was observed in the MITF/TFEB DKO (Figure 5I), and GLYCOLYSIS was modestly downregulated in the MITF/TFE3 DKO (Figure 5H). These data suggest TFEB restricts MITF-driven activation of glycolysis-related genes.

Finally, the INTERFERON ALPHA and GAMMA RESPONSE gene sets were downregulated in all single KOs, the TFE3/TFEB DKO (Figure 5G), and the TKO (Figure 5J). Comparing the MITF/TFEB DKO (Figure 5I) to the TKO (Figure 5J) suggests that in the absence of MITF and TFEB, TFE3 controls the INTERFERON RESPONSE gene sets.

A key conclusion is that TFE3 and TFEB behave very differently, a conclusion supported by comparing the genes significantly differentially regulated by the TFE3 KO or the TFEB KO in the MITF KO background (Figure S4H). GSEA (Figure 5K) revealed enrichment of the EMT, HYPOXIA, INTERFERON ALPHA, and GAMMA RESPONSE gene sets and robust downregulation of mTORC1 SIGNALING, MYC TARGETS, UNFOLDED PROTEIN RESPONSE, and OXIDATIVE PHOSPHORYLATION.

CyQuant assays revealed that proliferation was reduced in the MITF KO, increased in the TFE3 KO, and unaffected in the TFEB KO or TFE3/TFEB DKO (Figure S4J). By contrast, the MITF/TFE3 and MITF/TFEB DKOs and the TKO exhibited severely reduced proliferation. However, to establish cell lines, cells must proliferate. Consequently, any effect of each KO on proliferation may be underestimated.

The genes differentially regulated by combinations of MITF family members include both those directly bound by MITF, TFEB, and TFE3 and those where no binding was observed. However, regulation of genes by one factor but not another could arise from genes differentially bound by the transcription factors and not because they have fundamentally different roles in regulating genes commonly bound by all three. To address this issue, we analyzed the differential expression of genes in each KO cell line using only the 11,219 genes bound by all three factors (Figure 3F). The results show major differences in gene expression that fall broadly into 10 clusters (Figure S5A; Table S3). For example, cluster 7, enriched in genes implicated in EMT and transforming growth factor β (TGF-β) signaling (Figure S5B), is strongly upregulated in the MITF KO but not in the MITF/TFEB DKO or the TKO (Figure S5A). Similarly, genes in cluster 8, enriched in TGF-β signaling, are downregulated only in the MITF/TFE3 DKO, whereas cluster 1 genes, enriched in mTORC1 and tumor necrosis factor alpha (TNF-α) signaling, are only downregulated in the MITF/TFEB DKO and the TKO. The results strongly support the conclusion that genes bound by each MITF family member may nevertheless be differentially regulated by them alone or in combination.

MITF family genes differentially impact metabolism

The GSEA indicated that MITF controls glycolysis-related gene expression. We therefore undertook Seahorse assays on the parental and MITF family KO cell lines to measure the extracellular acidification rate (ECAR) and oxygen consumption rate (OCR), indirect measures of glycolysis and oxidative phosphorylation, respectively. The ECAR (Figures 6A, left, and 6B) was lowest in the parental cells and moderately increased in all KO cell lines, especially in the TFE3 KO and the MITF/TFEB DKO, with the TKO exhibiting the least increase. Treatment with oligomycin, an inhibitor of mitochondrial reversible ATP synthase that uncouples oxidative phosphorylation, revealed that all cells retained a glycolytic reserve proportional to basal glycolysis. Treatment with rotenone and antimycin A to block the electron transport chain had a moderate effect only in the TFEB/TFE3, MITF/TFE3, and TKO cell lines. Measurement of the OCR (Figures 6A, right, and 6C) revealed greater differences between cell lines. Whereas parental B16-F10 cells have a low basal OCR, a striking increase was detected in the MITF KO and, to some extent, in the MITF/TFE3 and MITF/TFEB DKOs. The TKO exhibited a reduced increase compared to the MITF KO or MITF/TFEB and MITF/TFE3 DKOs. Thus, while MITF suppresses oxidative phosphorylation, it is promoted by TFEB and TFE3 in the absence of MITF. The minor increase in basal OCR in the TFE3 and TFEB KOs was not significant compared to the TKO. Consistent with these data, the MITF KO produced 60% more ATP than the parental cells, with the most significant increase arising from mitochondrial ATP production (Figure 6D). For the remaining KO cell lines, the ratio of mitochondrial to glycolytic ATP production was similar to that of the parental cells. However, the increased mitochondrial ATP production by the MITF KO cells was reduced in the TKO, indicating that much of the increase in the MITF KO depends on TFEB and TFE3. In agreement, many genes in the HALLMARK OXIDATIVE PHOSPHORYLATION gene set were upregulated in the MITF KO but not in the MITF/TFEB DKO or the TKO (Figure 6E). Increased expression of these genes in the MITF KO is therefore dependent on TFEB.

Figure 6. — (A) Seahorse assays measuring ECAR and OCR in B16-F10 WT or KO cell lines. Error bars: SD; n = 3.

(B and C) Seahorse assays summarizing differential basal ECAR (B) and OCR (C) between B16-F10 WT or KO cell lines. Error bars: SD; n = 3; ns, non-significant, *p < 0.05, and **p < 0.01 (black, comparison with parental cells; red, comparison with TKO); Student’s t test.

(D) ATP production from glycolysis or oxidative phosphorylation by the indicated cell lines. Numbers indicate the percentage derived from each source. Error bars: SD; n = 3; ns, non-significant, *p < 0.05, and **p < 0.01; Student’s t test comparing mutants to B16-F10 WT cells.

(E–G) Triplicate RNA-seq showing relative gene expression in parental and B16-F10 KO cell lines of a subset from the HALLMARK OXIDATIVE PHOSPHORYLATION gene set (E) or indicated genes (F) or in 501mel cells in which MITF has been induced using 100 ng doxycycline (G).

(H) Western blot of 501mel cells expressing ectopic HA-tagged MITF induced with 100 ng doxycycline for 16 h.

(I) Triplicate RNA-seq showing relative gene expression in parental and B16-F10 KO cell lines.

See also Figure S6.

Differential regulation of key target genes by the MITF family

Multiple promoters drive the expression of distinct MITF isoforms in different cell types.¹⁰ All three factors bind the MITF gene (Figure S6A) at consensus sequences (TCACATGA or ACACGTGA) upstream of the melanocyte-specific MITF-M isoform. Inactivation of MITF increased MITF mRNA levels (Figure S6B), consistent with previous work,¹⁹ as well as the expression of TFEB and TFE3. The increased MITF mRNA in the MITF KO was abrogated in the MITF/TFEB DKO and further reduced in the TKO (Figure S6B). TFEB and TFE3 therefore positively regulate MITF expression in the absence of MITF. No binding by any MITF family member was detected at the TFEB and TFE3 genes (Figures S6C and S6D), and only one weak non-consensus binding site around 18 kb downstream of TFEB was identified. Any change in TFEB or TFE3 expression in the KOs is therefore likely indirect.

We next examined a set of genes relevant to melanocyte/melanoma biology known to be directly bound and positively regulated by MITF (Figure 6F), including Ppargc1a^16,17 and Scd1.¹⁸ The results were surprising. In the MITF KO, some genes implicated in melanocyte differentiation, such as Mc1r, Dct, Tyrp1, and Trpm1, were upregulated, while others, such as Tyr, Pmel, and Slc45a, were largely unchanged. Only Mlana, Gpr143, and Rab38a were moderately downregulated. This suggests that in proliferating B16-F10 cells, MITF is a negative regulator of some differentiation genes. This interpretation was confirmed using human 501mel melanoma cells expressing Dox-inducible murine MITF (Figures 6G and 6H), where induction of MITF suppressed all differentiation-associated genes. By contrast, pro-proliferation genes, such as SCD, PPARGC1A, NAMPT, and HK2, were upregulated. Although many differentiation genes were upregulated in the MITF KO, expression of these genes, including Scd1 and Ppargc1a, was downregulated in the MITF/TFEB DKO and the TKO (Figure 6F). Thus, in the absence of MITF, basal expression of these genes, which are all bound by all three MITF family members, was dependent on TFEB. Example binding profiles for TYR, MLANA, and SCD are shown in Figure 3J and for PMEL in Figure S6E.

That MITF is pro-proliferative in B16-F10 cells was reinforced by examining a broader set of proliferation-associated targets, including Cdk2¹³ and Met⁸²; the receptor tyrosine kinases Kit and Ngfr; and Nampt, Axin2, and Hk2, all of which are bound by all three family members. All were moderately downregulated in the MITF KO (Figure 6I), as well as in the MITF/TFE3 and MITF/TFEB DKOs and the TKO. These results together indicate that in some differentiation genes, MITF may play a repressive role in proliferating melanoma cells while promoting the expression of pro-proliferation genes.

MITF^High cells uptake long-chain fatty acids via fatty acid transporter proteins (FATPs) to fuel melanoma progression.^83–85 Inactivation of MITF or TFEB had little effect on the expression of Slc27a1 (Figure 6I), which encodes FATP1 and is bound by all MITF family members (Figure S6E). However, Slc27a1 expression is strongly downregulated in the TFE3 KO (Figure 6H).

Loss of MITF also upregulated two mTORC1 inhibitors, Sesn2^86–89 and Deptor,⁹⁰ consistent with MITF promoting mTORC1 signaling (Figure 1). Both genes are bound by all family members at consensus TCACGTGG (Sesn2) or TCATGTGA (Deptor) motifs (Figure S6E). Regulation of DEPTOR by MITF was confirmed in IGR37 cells by RT-qPCR following the depletion of MITF (Figure S6F) and by western blotting (Figure S6F, bottom). The elevated Sesn2 and Deptor expression observed in the MITF KO was not seen in the TKO, implicating other family members in their upregulation.

Interestingly, AXL, encoding a key receptor tyrosine kinase linked to therapy resistance,^29,30 is expressed in MITF^Low melanoma cells and can be activated by unsaturated fatty acids.⁸⁵ AXL is not bound by any MITF family member but is upregulated in the MITF/TFEB DKO (Figure 6H) but not in the TKO. Upregulation of AXL in the MITF/TFEB DKO is therefore TFE3 dependent.

We also observed that many genes in the HALLMARK INTERFERON ALPHA gene set were downregulated in the MITF KO but were strongly upregulated by TFE3 in the MITF/TFEB DKO (Figure S6G). As many are bound by MITF family members (Table S4), they may be directly regulated by changes in MITF family expression modulating the immune microenvironment.

MITF, TFEB, and TFE3 control tumor growth and immune cell infiltration

Subcutaneous transplantation into C57BL/6 mice revealed that tumor formation by the MITF/TFE family single KOs and DKOs was delayed (Figures 7A–7C), with the MITF KO and the DKOs being most affected. Following tail vein injection, lung colonization was severely reduced in the MITF KO cells but was less affected in the TFEB and TFE3 KO cells (Figures 7D and 7E). Because the immune landscape is dramatically different between human MITF^High versus MITF^Low tumors,⁹¹ we also examined immune infiltration into the subcutaneous parental and KO tumors. As expected, parental B16-F10 cells exhibited low levels of infiltration of all three immune cell types examined, consistent with the poor immune cell infiltration and immunogenicity of WT B16-F10 tumors (Figures 7F and 7G). Infiltration by CD8⁺ T cells increased in the TFE3 KO and TFE3/TFEB KO tumors but not in any other KOs. This suggests that TFE3 suppresses MITF-dependent CD8⁺ T cell infiltration.

Figure 7. — (A) Bioluminescent imaging of primary tumors 28 days after subcutaneous injection of luciferase-expressing B16-F10 parental or KO cells (n = 5). Firefly luciferin (120 mg/kg) was injected intraperitoneally. The scale bar of the bioluminescent image radiance (photons/s/cm²/sr) color scale was set to Min = 1.00e5 and Max = 1.00e6 for all pictures.

(B) Quantification of bioluminescence in primary tumors performed using the automatic ROI tool (n = 5), showing average and standard deviation of radiance. *p < 0.05 and **p < 0.01 when compared to WT cells using a two-tailed Student’s t test.

(C) Tumor growth in mice after subcutaneous inoculation of parental or KO B16-F10 cells. n = 5 mice/group. Tumor volume was measured using a caliper. *p < 0.05, **p < 0.01, and ns, not significant when compared to WT cells on the last day.

(D) Lung colonization of mice imaged 5 weeks after tail vein injection of 2e5 luciferase-expressing parental or KO B16-F10 cells. n = 5 per group.

(E) Quantification of bioluminescence in primary tumors performed using the automatic ROI tool (n = 5), showing average and standard deviation of radiance. **p < 0.001 and ns, not significant using a two-tailed Student’s t test.

(F) Immune cell infiltration of B16-F10 tumors determined using immunohistochemistry of paraffin-embedded sections stained with antibodies to detect T cells, macrophages, and NK cells. Arrowheads indicate examples of positive cells. Scale bars: 50 μm.

(G) Pathologist assessment of immune infiltration in B16-F10 WT and KO tumor sections. Scoring was done using 5 fields from 3 tumor sections.

(H) Model showing sequential changes in MITF, TFEB, and TFE3 expression following glucose limitation over time.

Macrophage infiltration is elevated in tumors derived from the TFE3 KO, MITF KO, and MITF/TFEB DKO. However, the increase in the TFE3 KO is moderately reduced if TFEB is also knocked out and is abrogated in the MITF/TFE3 DKO and the TKO.

No increase in natural killer (NK) cell tumor infiltration was observed in any of the single KOs or the MITF/TFEB DKO. However, there is a substantial increase in NK cells in the MITF/TFE3 DKO tumors and, to some extent, the TFE3/TFEB DKO tumors. As no increase is detected in the TKO, the increase in NK cell infiltration observed in the MITF/TFE3 or TFE3/TFEB DKOs is promoted either by TFEB or by MITF, respectively.

Finally, we note that the MITF KO tumors, unlike any other, exhibited a different structure with large gaps between clusters of tumor cells that may reflect differential cell-cell adhesion or extracellular matrix composition in the MITF KO cells, consistent with a previous report.⁷⁶

DISCUSSION

Whether functionally related transcription factors with identical DNA-binding specificity might perform distinct functions at the same target genes is a key question, especially relevant to cancer. Here, we use melanoma to examine the role of TFE3 and TFEB in cells that also express the melanocyte-specific isoform of MITF that is largely constitutively nuclear⁹² and not inhibited by mTORC1. The results were surprising.

Consistent with its pro-proliferative role, we found that MITF promotes mTORC1 signaling by suppressing the expression of PTEN and the mTORC1 negative regulators SESN2 and DEPTOR. Signaling downstream of mTORC1 is partitioned into canonical RHEB-dependent signaling to substrates like S6K and a non-canonical pathway where mTORC1 targeting of TFEB and TFE3 relies instead on RAGC/D and FLCN/FNIP2.⁹³ Paradoxically, activating mTORC1 mutations can trigger a negative feedback loop leading to hypophosphorylated nuclear TFEB/TFE3.^94,95 By promoting canonical mTORC1 signaling, it is possible that despite upregulating RRAGD and FNIP2, MITF may suppress non-canonical signaling to promote the largely nuclear TFEB and TFE3 observed in MITF^High melanoma cells. However, surprisingly, TFE3 is cytoplasmic in RAGD-negative IGR39 cells, raising the possibility that rather than cytoplasmic retention, TFE3 might be subject to increased nuclear export.

Remarkably, in response to glucose limitation, MITF and then TFEB are downregulated, leaving TFE3 to suppress cell death (Figure 7H). The low-glucose-mediated switch from TFEB to TFE3 may also be relevant to their activation by AMPK independently of mTORC1.⁹⁶ As the switch also operates in colon cancer cells, it seems likely it will occur in many cell types. For example, in macrophages, where TFEB and TFE3 cooperate to control inflammatory signaling,⁴² lipopolysaccharide treatment initially activates TFEB before its expression is lost, leaving TFE3 as the sole family member expressed.

Against expectation, while all MITF family members bind the same genes, likely at nucleosome-free regions,^97,98 their actions on those genes alone or in combination are not always the same. GSEA suggests that TFEB plays a broadly similar role to MITF but that TFE3 instead acts in opposition to MITF/TFEB, at least in some genes. Our conclusion is that upon encountering microenvironmental stresses, such as glucose limitation, MITF is downregulated via the integrated stress response (ISR) before TFEB transiently takes on many MITF functions to buy time for cells to restore homeostasis. If the stress remains unmitigated, TFEB is also suppressed, and TFE3 then promotes cell survival, oxidative phosphorylation, and upregulation of interferon alpha response genes.

Although MITF is widely assumed to be the key regulator of melanocyte differentiation genes, surprisingly, several differentiation-associated genes were upregulated by TFEB in the MITF KO and downregulated if MITF was overexpressed. Notably, in the same MITF KO cells, pro-proliferative MITF targets such as Cdk2 and Met were suppressed, while MITF-bound anti-proliferative genes such as Sesn2 and Deptor were upregulated. We hypothesize that in proliferative cells, MITF activates pro-proliferation genes but suppresses TFEB-driven activation of differentiation genes; by contrast, in differentiated cells, MITF will upregulate differentiation genes and suppress cell division. The nature of this MITF activity switch is unknown, but it may relate to the regulation of MITF co-factors by proliferation-associated post-translational modifications such as acetylation.^64,72

Previous work revealed that depletion of TFEB in melanoma cells caused reduced proliferation and lower glucose and glutamine uptake and, consequently, reduced glycolysis, glutaminolysis, and slower tumor growth in vivo.⁵⁰ By contrast, Li et al.⁹⁹ reported that BRAF inhibition increased TFEB-promoted autophagy, while a constitutively active TFEB S142A mutant slowed tumor growth and an S142E phosphomimetic mutant enhanced tumor growth. Our results are more in line with those of Ariano et al.⁵⁰ since tumor growth in the B16-F10 TFEB KO was moderately reduced, with a greater reduction in any DKO. Our GSEA also revealed an MITF-dependent increase in glycolysis-associated mRNAs in TFEB KO cells, perhaps compensating for the decreased glucose uptake and glycolytic enzyme activity observed by Ariano et al.⁵⁰ in TFEB-depleted cells. Notably the increased oxidative-phosphorylation-dependent ATP production in the MITF KO requires TFEB and TFE3 and is consistent with MITF^Low slow-cycling melanoma cells exhibiting elevated oxidative phosphorylation.^100,101 Taken together, our data implicate TFEB and TFE3 as playing a key role in the metabolic switch associated with loss of MITF.

Finally, we show that the MITF family controls the quality and quantity of immune infiltration. For example, inactivation of TFE3 greatly increased MITF-dependent CD8⁺ T cell and macrophage infiltration, while infiltration by NK cells was dramatically enhanced in the MITF/TFE3 and TFE3/TFEB DKOs. The results are consistent with evidence highlighting MITF, and by implication, TFE3 and/or TFEB, in control of the anti-tumor immune response.^81,102,103 Given the importance of immune infiltration in the response to immune checkpoint blockade, our results provide a clear path to future studies directed at dissecting the molecular mechanisms controlling the tumor immune microenvironment.

Limitations of the study

The MITF transcription factor family can homo- and heterodimerize¹⁰⁴ in vitro. However, we cannot establish whether heterodimerization contributes to their capacity to dictate distinct gene expression programs. However, Verastegui et al.¹⁰⁵ found no detectable heterodimerization between MITF and TFE3 in B16-F10 melanoma, and genetic analysis of MITF family mutant mice indicated that heterodimeric interactions are not essential for MITF-TFE function.¹⁰⁶ In general, the data are more consistent with expression, rather than dimerization, playing a key role in their differential activities.

MITF family members bind chromatin-free regions of the genome⁹⁷ with accessibility also dictated by TFAP2.⁹⁸ Our ChIP-seq was performed only in WT cells. However, loss of MITF and TFEB could lead to the acquisition of novel recognition sites for TFE3, as recently noted.⁷⁵ It is possible, therefore, that some TFE3/TFEB-regulated genes are bound only in the absence of MITF, leading to an underestimation of the number of genes directly regulated by MITF family members.

Finally, KO cell lines are selected for proliferation, and consequently, cells must bypass any block to proliferation induced by a KO. The assays used here may therefore underestimate effects on the cell cycle by the various MITF family KOs.

RESOURCE AVAILABILITY

Lead contact

Requests for further information and resources should be directed to and will be fulfilled by the lead contact, Colin R. Goding (colin.goding@ludwig.ox.ac.uk).

Materials availability

All unique reagents generated in this study may be obtained from the lead contact with a completed materials transfer agreement if necessary.

Data and code availability

All ChIP-seq and RNA-seq datasets have been deposited at the Gene Expression Omnibus (GEO) and are publicly available as of the date of publication. Accession numbers are listed in the key resources table.
Original western blot images have been deposited at Mendeley at https://doi.org/10.17632/p6pr9tv6kv.1 and are publicly available as of the date of publication.
This paper does not report original code.
Any additional information required to reanalyze the data reported in this paper is available from the lead contact upon request.

KEY RESOURCES TABLE.

REAGENT or RESOURCE	SOURCE	IDENTIFIER
Antibodies
Mouse monoclonal anti-MITF (C5)	Abcam Cat# ab12039	RRID: AB_298801
Mouse monoclonal anti-GAPDH (6C5)	Santa Cruz Biotechnologies Cat# sc-32233	RRID: AB_627679
Mouse monoclonal anti-PTEN (2F4C9)	ThermoFisher Scientific Cat#32–5800	RRID: AB_2533087
S6	Cell Signaling Cat#2217	RRID: AB_331335
pS6	Cell Signaling Cat#2211	RRID: AB_331679
ACTIN (AC-40) mouse monoclonal	Sigma-Aldrich Cat# A4700	RRID: AB_2560935
RRAGD	Cell Signaling Cat# 4470	RRID: AB_1950380
FNIP2	Cell Signaling Cat#57612	RRID: AB_2799530
TFEB Hs	Cell Signaling Cat# 4240	RRID: AB_11220225
TFE3	Sigma-Aldrich Cat# HPA023881	RRID: AB_1857931
TFE3 (for Immunofluorescence)	Cell Signaling Cat#37785S	RRID: AB_2799119
TFEB Mm	Cell Signaling Cat# 83010	RRID: AB_3105800
DEPTOR	Novus Cat# NBP1–49674	RRID: AB_10011798
HA	Roche Cat#11666606001;	RRID: AB_514506
HA	Sigma-Aldrich Cat#H3663	RRID: AB_262051
HA	Cell Signaling Technology Cat#2367	RRID: AB_2314619
ERK 2	Santa Cruz Biotechnology Cat#sc-153	RRID: AB_2141293
ERK 2	Santa Cruz Biotechnology Cat#sc-1647	RRID: AB_627547
GAPDH	Santa Cruz Biotechnology Cat#sc-32233	RRID: AB_627679
F4/A80	Bio-Rad Cat# Cl A3–1	RRID: AB_394533
NKp46	Thermo Fisher Cat# PA5–79720	RRID: AB_2746835
CD8a	Abcam Cat# ab4055	RRID: AB_304247
Anti-Mouse Alexa Fluor 488	ThermoFisher Scientific Cat#A11001	RRID: AB_2534069
Anti-Mouse Alexa Fluor 546	ThermoFisher Scientific Cat#A10036	RRID: AB_11180613
Anti-Rabbit Alexa Fluor 488	ThermoFisher Scientific Cat#A11008	RRID: AB_143165
Anti-Rabbit Alexa Fluor 546	ThermoFisher Scientific Cat#A10040	RRID: AB_2534016
Alexa Fluor 488 donkey anti-rabbit IgG (H + L)	ThermoFisher Cat#A21206	RRID: AB_2535792
Bacterial and virus strains
Subcloning Efficiency DH5a bacteria	Invitrogen	Cat#18265–017
Chemicals, peptides, and recombinant proteins
Torin	Cayman Chemicals	Cat# 10997
Geneticin	Gibco	Cat# 10131027
Puromycin	Gibco	Cat# A1113802
Doxycycline	Sigma-Aldrich	Cat# D9891
DMSO	Sigma-Aldrich	Cat# D2650
cOmplete^™ ULTRA Protease Inhibitor Cocktail	Sigma-Aldrich	Cat# 5892988001
Glutamax DMEM media	GIBCO	Cat#31966021
RPMI 1640 media	GIBCO	Cat#21875–034
HBSS	GIBCO	Cat#21090–022
DMEM	GIBCO	Cat#11965
RPMI NO GLUCOSE	GIBCO	Cat#11879–020
DMEM no glucose	GIBCO	Cat#11966–025
Normal Fetal Bovine Serum	Sigma	Cat#F4135
Lipofectamine CRISPRMAX	ThermoFisher	Cat# CMAX00001
OptiMem	GIBCO	Cat#31985–062
FuGENE6	Promega	Cat#E2692
BSA	Sigma-Aldrich	Cat#A9647–100G
Lipofectamine RNAiMAX	ThermoFisher	Cat# 13778100
Sodium Azide	Sigma-Aldrich	Cat#S2002
Critical commercial assays
ECL detection kit	Amersham Biosciences	Cat# RPN2106
RNA extraction RNeasy Mini Kit	Qiagen	Cat#74106
MycoAlert^™ mycoplasma detection kit	Lonza	Cat#LT07–318
PureLink^™ Genomic DNA Mini Kit	Invitrogen	Cat# K182002
QIAprep Spin Miniprep Kit	QIAGEN	Cat# 27106
Deposited data
RNA-seq from MITF, TFEB and TFE3 B16-F10 KO cell lines	This paper	GSE270594
RNA-seq from 501mel cell lines	This paper	GSE277250
TFEB ChIP-seq	This paper	GSE270585
TFE3 ChIP-seq	This paper	GSE270590
shMITF RNA-seq	This paper	GSE255256
RNA-seq from inducible CTNNB1 in IGR37 cells	Louphrasitthiphol et al.¹⁰⁷	GSE136803
CUT&RUN in SKMEL28	Chang et al.⁷⁵	GSE273815
Experimental models: Cell lines
IGR37 Human melanoma cell line (female)	Obtained from Luisa Lanfrancone, IEO	Aubert et al.¹⁰⁸
IGR39 Human melanoma cell line (female)	Obtained from Luisa Lanfrancone, IEO	Aubert et al.¹⁰⁸
501mel Human melanoma cell line. This cell line by STR analysis is 624 mel (male) but is widely called 501mel cells in many publications from different labs.	Obtained from Ruth Halaban, Yale	Zakut et al.¹⁰⁹
A375M Human melanoma cell line (male)	Obtained from ATCC	Giard et al.¹¹⁰
SKMEL28 Human melanoma cell line (male)	Obtained from ATCC	Fogh et al.¹¹¹
SKMEL30 Human melanoma cell line (female)	Obtained from ATCC	Carey et al.¹¹²
HBL Human melanoma cell line (female)	Obtained from Ghanem Ghanem, Brussels	Ghanem et al.¹¹³
WM793 Human melanoma cell line (male)	Obtained from the ATCC	Herlyn et al.¹¹⁴
WM266.4 Human melanoma cell line (female)	Obtained from the ATCC	Westermark et al.¹¹⁵
WM115 Human melanoma cell line (female)	Obtained from the ATCC	Westermark et al.¹¹⁵
WM9 Human melanoma cell line (male)	Obtained from the ATCC	Herlyn et al.¹¹⁶
MeWo Human melanoma cell line (male)	Obtained from the ATCC	Cornil et al.¹¹⁷
CHL-1 Human melanoma cell line (female)	Obtained from the ATCC (Derived from RPMI 7932 cells)	Patent US5017478A
HT-29 Human Colon Cancer (female)	Obtained from the ATCC	Fogh and Trempe¹¹⁸
B16-F10-Luciferase mouse melanoma cell line (male)	Obtained from Luis Sanchez del Campo	Sanchez-Del-Campo et al.⁸¹
B16-F10-Luciferase MITF KO mouse melanoma cell line (male)	Obtained from Luis Sanchez del Campo	Sanchez-Del-Campo et al.⁸¹
Experimental Models: Mice
Female C57BL/6	Envigo (Barcelona, Spain)	N/A
Oligonucleotides
siRNA control	Qiagen	Cat# 1027281
siRNA MITF 1 5′-AAAGCAGTACCTTTCTACCAC	Sigma	Falletta et al.²⁵
siTFE3	Santa Cruz	Cat# sc-38507
TFEB sgRNA 5′-AGTACCTGTCCGAGACCTAT	ThermoFisher A35533 CRISPR775049_SGM	This paper
TFE3 sgRNA 5′-GGTACTGTTTCACCTGCTGC	ThermoFisher A35533 CRISPR381077_SGM	This paper
Tfeb sgRNA 5′-TGGACACGTACTGTCCACCT	ThermoFisher A35533 CRISPR557759_SGM	This paper
Tfe3 sgRNA 5′-AGCGTGTAGGGTTCTCGAGG	ThermoFisher A35533 CRISPR284202_SGM	This paper
DEPTOR qRT PCR Forward 5′-ACTCATGACAGTCCCTTCTGC	N/A	This paper
DEPTOR qRT PCR Reverse 5′-GACACGATCTTGATCTCCTTGCT	N/A	This paper
PTEN qRT PCR Forward 5′-TGGCACTGTTGTTTCACAAGAT	N/A	This paper
PTEN qRT PCR Reverse 5′-TAGCTGGCAGACCACAAACT	N/A	This paper
MITF qRT PCR Forward 5′-AGGTACTTTGATTCCCAAGTCA	N/A	This paper
MITF qRT PCR Reverse 5′-AAGTTCTTTTGCGCGTTGCT	N/A	This paper
Recombinant DNA
Plasmid Piggybac expression system	N/A	Magnusdottir et al.¹¹⁹
Software and algorithms
MetaQuant	N/A	Bunk et al.¹²⁰
ImageJ	https://imagej.nih.gov/ij/	N/A
Cutadapt	N/A	Martin¹²¹
STAR	N/A	Dobin et al.¹²²
Homer	N/A	Heinz et al.¹²³
EdgeR	N/A	Robinson et al.¹²⁴
TreeView	N/A	Saldanha et al.¹²⁵
javaGSEA v.4.3.2	N/A	Subramanian et al.¹²⁶
emmeans v1.10.7	cran.r-project.org	N/A
nlme v.3.1–166	cran.r-project.org	N/A
fastqc v.0.11.9	GitHub - s-andrews/FastQC	N/A
trim_galore v.0.6.5	N/A	Martin et al.¹²¹
clusterProfiler v4.6.2	Bioconductor	N/A
DOSE v3.24.2	Bioconductor	N/A
enrichplot v.1.18.4	Bioconductor	N/A
VennDiagram v.1.7.3	cran.r-project.org	N/A
ggplot2 v.3.4.4	cran.r-project.org	N/A
GraphPad Prism 7	https://www.graphpad.com	N/A
Perseus	N/A	Tyanova et al.¹²⁷

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STAR★METHODS

EXPERIMENTAL MODEL AND STUDY PARTICIPANT DETAILS

Human tumor samples and immunohistochemistry

Melanoma patient samples were obtained from surgical resections of patients diagnosed with melanoma at Hospital Clínico San Carlos (Madrid, Spain). This study using human melanoma tissue was reviewed and approved by the Institutional Review Board (IRB) of Clínico San Carlos Hospital on June 25, 2021 (C.I. 21/498-E). All patients gave written informed consent for the use of their biological samples for research purposes. Fundamental ethical principles and rights promoted by Spain (LOPD 15/1999) and the European Union (2000/C364/01) were followed. Patient data were processed according to the Declaration of Helsinki (last revision 2013) and Spanish National Biomedical Research Law (14/2007, July 3). Patient characteristics are described in the table below. Ethnicity was not recorded.

Patient characteristics	N (%)

Gender
Male	1 (33.3%)
Female	2 (66.6%)
Age, n(%)
<60	1 (33.3%)
>60	2 (66.6%)
Breslow (median, mm)	2.7
Clark level, n(%)
I	0 (0%)
II	0 (0%)
III	0 (0%)
IV	3 (100%)
Histologic type, n (%)
NM	2 (66.6%)
NA	1 (33.3%)
Ulceration, n (%)
Yes	1 (33.3%)
No	0 (0%)
NA	2 (66.6%)
Metastasis development
Yes	2 (66.6%)
No	1 (33.3%)

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Paraffin-embedded tumor samples were sliced into 5 μm sections for staining. Slides were deparaffinized by incubation at 60°C for 10 min and then incubated with PT-Link (Dako, Agilent) for 20 min at 95°C at pH 6.0 to detect TFEB. Slides in slide-holders were incubated with peroxidase blocking reagent (Dako, Agilent) and then with the following dilutions of antibodies: anti-TFEB (1:50) overnight at 4°C. Slides were incubated for 20 min with the appropriate HRP-conjugated anti-Ig (EnVision, Dako, Agilent). Sections were then visualized with the HRP Magenta (Dako, Agilent), and counterstained for 5 min with Harrys’ Haematoxylin (Sigma Aldrich, Merck). Photographs were taken with a Zeis microscope with a 20× objective.

Mice and mouse tumor immunohistochemistry

Healthy, female C57BL/6 mice, 6 weeks of age, were purchased from Envigo (Barcelona, Spain). All mice were housed in pathogen-free, ventilated cages in the animal facility at the University of Murcia in accordance with the Spanish legislation and directives of the European Community. All animal procedures were approved by the Ethics Committee of the University of Murcia and the Direccion General de Ganaderia y Pesca, Comunidad Autonoma de Murcia (Project reference A13151101). Mice were monitored daily and sacrificed before any signs of distress. Mice were inoculated with 2 × 10⁵ B16-F10 cells subcutaneously or in the tail vein. The size of the subcutaneous tumors was measured three times weekly for the duration of the experiment with calipers. All tumors were analyzed using the IVIS Imaging System (Caliper Life Sciences, Hopkinton, MA, USA). Details of numbers of control and test mice as well as statistical analysis used are provided in the legend to Figure 7.

To establish the degree of inflammatory infiltrate (T-CD8⁺ lymphocytes, NK cells and macrophages), an indirect biotin free HRP immunohistochemistry was carried out in formalin-fixed and paraffin embedded sections from tumors. Briefly, after deparaffination and rehydration, sections underwent a demasking antigen procedure (Dako High pH antigen retrieval solution, Agilent Tech., Barcelona, Spain). Endogenous peroxidase was then blocked using a commercial solution (Dako Peroxidase Blocking, Agilent). Sections were incubated overnight at 4°C with the primary antibodies (polyclonal rabbit anti-CD8, Abcam, Oxford, UK; polyclonal rat anti-F4/80, BioRad, Madrid, Spain and polyclonal rabbit anti-NKp46 (CD335, PA5–79720; ThermoFisher Scientific), and with the anti-rabbit secondary HRP-labelled polymer (Vector ImmPress, Vector Labs., Newark, CA, USA). To avoid false positive staining due to the endogenous melanin pigment located within tumor cells, the immunoreaction was revealed using a special HRP substrate (Vector VIP, Vector Labs.) which labels positive immunoreactions with a dark-violet stain. Finally, sections were contrasted using fast red solution, dehydrated, cleared and mounted. All sections were examined with a standard brightfield microscope (Zeiss Axio A10, Carl Zeiss, Jena, Germany) and representative images were obtained with a high-resolution digital camera (Axiocam 506 color, Carl Zeiss) by using a commercial software (Zeiss Zen Lite, Carl Zeiss). To determine the degree of inflammatory infiltrate a semiquantitative scale was used as follows: − (negative), + (sporadic), ++ (moderate) and +++ (intense) infiltrate.

Cell lines

The origin and sex of all cell lines used is provided in the Key Resource table. All cells were subject to authentication using STR analysis using Eurofins authentication service, and routinely tested by mycoplasma via PCR, following a previously established and published protocol¹²⁸ or use of the MycoAlert PLUS Mycoplasma Detection Kit (Lonza). Only mycoplasma-negative cells were used.

All human melanoma cell lines were grown in Roswell Park Memorial Institute (RPMI) medium, except HBL which were grown in Dulbecco’s Modified Eagle Medium (DMEM), supplemented with 10% Fetal Bovine Serum (FBS), 50 units/mL penicillin and 50 units/mL of streptomycin. All human melanoma cell lines contain the BRAF^V600E oncogenic mutation, except HBL and CHL-1 cells which possess wild-type BRAF. The mouse melanoma cell line B16-F10 was grown in DMEM, supplemented with 10% FBS as well as 50 units/mL penicillin and 50 units/mL of streptomycin. All cells were grown as a monolayer and passaged once they reached around 80%–90% confluency.

For cell lines stably transfected with PiggyBac doxycycline-inducible expression vectors, selection and maintenance was performed via the addition of 1 mg/mL of geneticin (G418) and 1 μg/mL of puromycin. Selection was first performed with geneticin for the first week, any live cells then allowed to recover until the second weekly selection treatment with puromycin. Cells were kept at 37°C with 10% CO₂ and grown in T75 culture flasks.

For the TFEB and TFE3 genes the top 10 most efficient sgRNAs in terms of on- and off-site efficacy identified using the Broad Institute Benchling software were evaluated using the Integrated DNA Technologies (IDT) Cas9 Checker software to choose the most efficient gRNA from the list. To generate target gene knockout cell lines, the Lipofectamine CRISPRMAX method (ThermoFisher) was followed. Cells were plated on Day 0 and allowed to adhere in a 6-well plate to reach 35% confluency when transfected on Day 1. 125 μL of Opti-MEM, 6.25 μg of Cas9 nuclease, 1.2 μg of synthetic gRNA and 12.5 μL of Cas9 Plus reagent were added to Tube 1 in that order and allowed to incubate at room temperature. The sequences of the guides were: TFEB 5′- AGTACCTGTCCGAGACCTAT; TFE3 5′GGTACTGTTTCACCTGCTGC; Tfeb 5′– TGGACACGTACTGTCCACCT; Tfe3 5′– AGCGTGTAGGGTTCTCGAGG. Meanwhile, in Tube 2, 125 μL of Opti-MEM was gently mixed with 7.5 μL of CRISPRMAX reagent and incubated for no more than 3 min. The Lipofectamine mix was then added to the Cas9-gRNA ribonucleoprotein (RNP) complex, mixed well by pipetting, and incubated for 15 min at room temperature. Finally, fresh medium was added to the 6-well plate and the 250 μL lipofectamine-RNP complex mix was added to each well in a dropwise manner. Cells were then incubated a further 24–48 h before screening for monoclonal knockouts. Cells were trypsinized, collected into fresh medium in 5 mL total of suspension, counted using an automated counter and diluted accordingly until reaching about 5×10⁵/mL. At this point, cells were serially diluted into a final 1 mL suspension containing about 1000 cells. Clones were distributed into a 96-well plate, with an estimated density of 0.96 clones per well in a final volume of 200 μL and left to grow for about 2 weeks or until visible colonies arising from single clones were seen. At this stage, cells were trypsinized and expanded until enough material could be obtained for Western Blotting to validate the monoclones. Secondary confirmation of knockout status was performed by extracting genomic DNA from monoclones and sending the PCR-amplified target sequence for Sanger sequencing, provided as a service from Eurofins, to ascertain the genomic event that occurred as a result of the Cas9 nuclease cutting the genome at the gRNA target site.

Bacterial strains

For general cloning purposes, the E. coli sub-cloning efficiency DH5α competent cells (ThermoFisher Scientific, Cat No. 18265017), were used. The genotype of the DH5α E. coli strain is F⁻ Φ80lacZΔM15 Δ(lacZYA-argF) U169 recA1 endA1 hsdR17(r_k⁻, m_k⁺) phoA supE44 thi-1 gyrA96 relA1 λ⁻.

METHOD DETAILS

Cell fractionation

Cells were grown to approximately 70–80% confluency. Subcellular fractionation was performed using the Rapid, Efficient And Practical (REAP) method. Briefly, cells were washed in ice-cold 1X PBS before being scraped into 200 μL of ice-cold 1X PBS containing 0.1% NP-40 (1 tablet of the cOmplete Protease Inhibitor Cocktail (Roche, Cat No. 11697498001) and 1 tablet of the PhosSTOP Phosphatase Inhibitor Cocktail (Roche, Cat No. 4906837001) were added immediately before use) and triturated 5 times using a Gibson P200 pipette. After, 60 μL was removed and transferred to a 1.5 mL Eppendorf tube labeled ‘whole cell fraction’. The remaining 140 μL was centrifuged using a table-top centrifuge (10 s, 5000×g, 4°C). After centrifugation, 60 μL of the supernatant was removed and transferred to a 1.5 mL Eppendorf tube labeled ‘cytoplasmic fraction’. The remaining supernatant was removed, leaving only the insoluble pellet. The pellet was washed by resuspending in 200 μL of ice-cold 1X PBS containing 0.1% NP-40 before centrifuging using a table-top centrifuge (10 s, 5000×g, 4°C). The supernatant was removed and discarded before resuspending the pellet in ice-cold 1X PBS containing 0.1% NP-40 and labeled ‘nuclear’ fraction. The ‘whole cell’ and ‘nuclear’ fractions were sonicated for 30 s at 4°C. After, 20 μL of 4X Laemmli buffer was added to all samples before boiling at 95°C for 5 min. All samples were stored at −20°C.

Cell cycle and DNA fragmentation analyses

Cells were transfected with 20 nM siRNA (siTFE3 #sc-38507, or siCTRL (QIAGEN # 1027281)), in case of further treatments (-Glc) for another 6 h before samples collected in 1.2 mL DPBS (no Mg²⁺/Ca²⁺ + 5% FBS and fixed in 70% EtOH with gentle shaking at 4°C, 1 h. Cells were stained in 250 μL staining solution (DPBS +0.5 mg/mL PI, 100 μg/mL (ThermoFisher P3566) RNase A (Roche10109169001), 0.05% Triton X-100) at 37°C, 45’. Following one wash in 1 mL PBS, cells were resuspended in 250–500 μL DPBS +5% FBS and analyzed on flow cytometry (SONY MA900). The cell cycle was profiled using FlowJo (v10.8.1) using Watson model. DNA fragmentation (sub-G1 population) was defined as population with PI signals below the G1 for siTFE3 or directly from Dean-Jett-Fox model fitting.

Western blotting

Whole cell lysate was prepared by adding an appropriate amount of 1x Laemmli Sample Buffer (62.5 mM Tris-HCl, pH 6.8, 2% Sodium Dodecyl Sulfate, 10% glycerol, 5% β-mercaptoethanol, 0.01% bromophenol blue) directly to the cells in the plate, usually 200 μL for one well of a 6-well plate at 80%–90% confluency. The whole lysate was then transferred to a 1.5 mL Eppendorf tube and boiled at 95°C for 10 min before analysis. 5%–10% of the total sample was used in each lane.

Proteins were analyzed via sodium dodecylsulphate polyacrylamide gel electrophoresis (SDS-PAGE) using 10% acrylamide from a 30% stock solution with 37.5:1 acrylamide to bis-acrylamide ratio (Severn Biotech). To allow fine resolving of specific phosphorylated proteins from their non-phosphorylated counterpart, an acrylamide stock with a ratio of 200:1 acrylamide to bis-acrylamide was used (Severn Biotech).

Samples were run/separated at 140V for 90 min in 1x Running Buffer (25 mM Tris, 192 mM glycine, 0.1% SDS). Precision Plus Dual-Colour Standard (BioRad) was used as indicated by the manufacturer was used as a size marker. After SDS-PAGE proteins were transferred unto a nitrocellulose membrane (GE Amersham) in a wet transfer tank (BioRad) filled with 1x Transfer Buffer (25 mM Tris, 192 mM glycine, 20% ethanol) and transferred at a constant amperage of 400 mA for 75 min. Membranes were then placed in 50 mL Falcon tubes and blocked at room temperature for 60 min in 5% Bovine serum albumin (BSA) in Tris-buffered saline (TBS) containing 0.1% Tween (TBS-T). Membranes were then briefly rinsed with TBS-T before proceeding to primary antibody incubation, also performed in 5% BSA in TBS-T. Most primary antibodies were used in a 1:1000 dilution while housekeeping antibodies were diluted 1:5000. Overnight incubation with primary antibodies was performed by leaving the 50 mL Falcon tubes rolling and incubating in the cold room at 4°C. The following day, membranes were rinsed twice for 10 min before incubation with HRP-conjugated secondary antibodies diluted 1:5000 in 5% milk in TBS-T at room temperature for 120 min. Afterward, membranes were again rinsed and washed 2 times for 10 min, at which point enhanced chemiluminescence reagent (ECL, GE Amersham) was incubated on the membranes for 5 min before the membranes were exposed to X-ray film (Fujifilm) and developed.

Immunofluorescence

For immunofluorescent experiments, cells were plated on circular glass coverslips (VWR) and grown until reaching 70–80% confluency in 24-well plates. Medium was aspirated and cells washed with 1x PBS at room temperature and then fixed on the plate with 4% paraformaldehyde (PFA) in 1x PBS for 10 min at room temperature. After fixation, PFA was aspirated, and coverslips rinsed 3 times with PBS followed by permeabilization with 0.2% Triton X-100 in PBS at room temperature for 10 min with agitation on a shaker. Permeabilization buffer was then aspirated, and fresh PBS was added to the wells to rinse the fixed cells on the coverslips before blocking for 1 h at room temperature with 4% BSA in PBS in a shaker. Afterward, coverslips were, dried and placed face down on a PARAFILM M (Sigma) sheet with 50 μL of primary antibody solution, consisting of primary antibody normally diluted 1:100 in 4% BSA in 1x PBS. Antibody incubation was either performed for 2 h at room temperature or overnight at 4°C, depending on affinity, concentration and ultimate signal obtained using the primary antibody. Coverslips were then placed into in a new sterile 12-well plate and washed 3x with PBS for 5 min each, before simultaneous incubation with secondary antibodies and DAPI, diluted both 1:500, face down in the parafilm with a light-protective cover. Once the secondary incubation was complete, coverslips were again transferred to a plate and washed with PBS for 5 min 3x times, dried and finally mounted on glass microscope slides (VWR) with MOWIOL (Millipore) mounting medium. Images were then taken with a confocal Zeiss LSM710 or LSM980, with image acquisition performed with the matching proprietary Zen software (Zeiss). Image processing was for presentation and publication purposes was done via Photoshop. Antibodies used are listed in key resources table.

Proliferation/survival assays

A DNA content-based cell proliferation assay that is independent of cellular metabolic status was carried out using CyQUANT Direct Cell Proliferation Assay (ThermoFisher, #C35011) as per manufacturer protocol using media only for background subtraction.

Seahorse analysis

The oxygen consumption rate (OCR) and extracellular acidification rate (ECAR) were measured using a Seahorse XF96 extracellular flux analyzer (Seahorse Bioscience, North Billerica, MA, USA). Briefly, 7,000–14,000 B16-F10 cells were seeded in XF96 well plates with the objective of achieving approximately 75% confluence and homogeneous distribution. On the following day, the medium was replaced with fresh Seahorse XF medium without phenol red supplemented with 1 mM pyruvate, 2 mM glutamine, and 10 mM glucose and incubated one hour in a CO₂-free incubator to guarantee precise measurements of extracellular pH. Metabolic activity was analyzed using an Agilent Seahorse 96XF device and respective kits. Glucose pyruvate oxidation stress, Glutamine oxidation stress and Glycolytic rate tests were performed as described in the manufacturer’s protocols respectively (Kit 103673–100, Kit 103674–100 and Kit 103344–100). Respiratory parameters were calculated using the Seahorse Wave Desktop Software (Agilent Technologies).

RNA-seq

Cells were allowed to reach 80% confluency in a 6-well plate before total RNA was extracted using RNeasy kit (QIAGEN #74106) as per manufacturer protocol. 500 ng total RNA were used for library preparation using QuantSeq Forward kit (LEXOGEN #015.96) according to the manufacturer’s protocol before sequencing by the Wellcome Trust genomic service, Oxford.

ChIP-seq and CUT&RUN

The ChIP protocol used here was the same as that previously used for HA-MITF ChIP seq. Cells were grown in 15 cm² dishes to reach 70–80% confluency with each replicate making up to 30 dishes. Cells were washed with 1x PBS and trypsinised with 4 mL of trypsin before being collected with 20 mL of warm medium into a 50 mL Falcon placed on ice. Cells were then centrifuged at 800 g for 4 min at 4°C. The medium was aspirated, and the cell pellet resuspended in 1 mL of ice-cold 1x PBS, to which 35 mL of ice-cold 1x PBS 0.4% PFA was added for crosslinking proteins to the DNA. The Falcon tubes were then kept on a roller for 10 min at room temperature before the crosslinking reaction was stopped by adding 4 mL of 2 M Glycine (0.2 M final concentration) to quench the PFA for a further 10 min at room temperature on the roller. Tubes were then centrifuged at 1500 g for 10 min at 4°C and the supernatant removed. At this stage, the cell pellets were frozen at −80°C. Then 1 mL of fresh ChIP lysis buffer (50 mM Tris-HCl (pH 8.0), 10 mM EDTA, 10 mM sodium butyrate, 1% SDS, 4×PIC (Roche; Cat#05056489001)) was added to the pellets. PIC and PhosStop were added to the buffer just immediately prior to use. Replicate lysates were pooled and broken down by using a 25 G needle and a 20 mL syringe, and aliquoted into Covaris 1 mL vials that were sonicated in an S220 sonicator (Covaris) for 15 min at 160 W, with 5% duty cycle and 200 cycles per burst. Sonicated DNA was then transferred from the Covaris vessel to a 1.5 mL Eppendorf tube and centrifuged at 13,000 g for 10 min at 4°C. The cleared supernatant was then transferred to a fresh 1.5 mL Eppendorf tube stored at −80°C after two 10 μL aliquots were transferred to a new tube. One aliquot was kept as a 1% input control and frozen at −80°C. The second aliquot was reverse crosslinked overnight to check for DNA fragment size by running the purified sample on a 2% agarose gel to confirm successful sonication and a desired size of fragment size of 200–400 bp. Once correct fragmentation had been achieved, DNA samples were thawed at 4°C and diluted with ChIP dilution buffer to 9 mL in a 50 mL Falcon tube. Next, 120 μg of HA antibody (Roche) was used per 30 dishes and the 50 mL Falcons left rotating and incubating overnight in the cold room at 4°C. In parallel, using an Eppendorf magnetic rack, 600 μL of Protein G Dynabeads were washed, resuspended and ultimately blocked overnight in a 1.5 mL tube with ChIP dilution buffer containing 0.5 mg/mL of BSA (Sigma) at 4°C in a rotator in the cold room. For the pulldown, blocked Dynabeads were added to the sample and rotated for 60 min at 4°C in the cold room, before being centrifuged for 10 min at 1500 g and 4°C. The supernatant was removed and all further washing steps carried out with the use of a microcentrifuge tube magnetic rack in the 4°C cold room, with the beads transferred to a fresh new tube after each washing step. Beads were resuspended in 1 mL of ChIP low salt wash buffer and transferred to a new 1.5 mL microcentrifuge tube, washed again and resuspended in another 1 mL of ChIP low salt wash buffer before being placed in a new tube. Beads were then washed twice more with ChIP low salt wash buffer followed by 2x washes with ChIP high salt wash buffer, then 2x washes with LiCl wash buffer. Finally, ChIP samples were simultaneously eluted with freshly made microwaved elution buffer and reverse cross-linked overnight in a Eppendorf shaker set at 55°C and 600 rpm, to release the proteins from the beads and the DNA fragments from the proteins. ChIP DNA was recovered using a PCR purification kit according to the manufacturer’s instructions, with the caveat that the first elution step was made with 30 μL for ChIP-seq library preparation and the second elution with 120 μL was kept for ChIP-qPCR. ChIP and Input samples were tested on Qubit and Bioanalyzer to confirm amount and integrity of the samples before being sent for sequencing.

CUT&RUN data for TFE3 and MITF in SKMEL28 WT and MITF KO cells was generated by Chang et al.¹⁰⁵ from the MITF KO cell lines established by Dilshat et al.⁷⁵

Bioinformatics analyses

B16-F10 RNA-seq and TFE3 ChIP-seq libraries were sequenced on an Illumina NovaSeq 6000. TFEB ChIP-seq libraries were sequenced on an Illumina HiSeq 4000. 501mel RNA-seq were sequenced on NextSeq 550. Base calling and demultiplexing were carried out by the Oxford Genomic Center Service, University of Oxford.

Fastq reads for each sample were sequenced across multiple flow cells (except B16-F10 RNA-seq libraries which were sequenced on a single flow cell) and were stitched on UNIX and quality controlled using fastqc (v.0.11.9).

ChIP-seq samples were adaptors and 2 colour-trimmed as paired-end reads while RNA-seq were also trimmed of poly-A as single-end reads using trim_galore (v.0.6.5; running python-base v.3.6.10, cutadapt v.2.10, java v.17.0.1, fastqc v.0.11.9 and pigz v.2.4).¹²¹

B16-F10 trimmed reads were mapped to the Mus musculus reference genome (mm39) with STAR (v.2.7.8a).¹²² 501mel trimmed reads were mapped to the Human reference genome (hg38) with STAR (version c.2.7.8a). RNA-seq normalisation and differential gene expression analyses were performed as previously described^18,19,107 using edgeR glmQLFTest. GSEA analyses were performed using pre-ranked lists derived from edgeR glmQLFTest −log₁₀(p-value)/sign(logFC) using GSEA (v.4.3.2) against MSigDB v2022.1. Dot blot of GSEA analyses were generated using pre-ranked list above using the Bioconductor packages clusterProfiler (v4.6.2), DOSE (v3.24.2). Genesets from MSigDB were imported using the package msigdbr (v7.5.1).

TFEB and TFE3 ChIP-seq trimmed fastq files were mapped using STAR (v.2.5.1b or v.2.7.8a, respectively)¹²² against the human hg38 index. Peak calling, de novo motif identification, genome ontology analyses were performed using Homer as previously described.^72,107 Normalised bigwig files of aligned reads were generated using Homer for visualisation on the UCSC genome browser as MultiWig hubs. The peaks calling parameter were: FDR rate threshold = 0.001, Fold over input = 4.00, Poisson p-value over input = 1.00e-04, Fold over local region = 4.00, Poisson p-value over local region = 1.00e-04 and variable parameters (modeled according to each dataset as these are sequencing depth dependent), FDR effective Poisson threshold and FDR tag threshold.

Data visualisation

Overlapping peaks were visualised using a R package VennDiagram (v.1.7.3). Genome ontology, box and jitter plots, volcano plots and peak correlation plots were generated using an R package ggplot2 (v.3.4.4). Heatmaps and hierarchical clustering of normalised gene expression were performed using an R package pheatmap (v.1.0.12). Tag density of ChIP-seq datasets were visualised using the java package TreeView (v.1.1.6).¹²⁵

GSEA dot plots were generated using the Bioconductor package enrichplot (v.1.18.4) using the cut-off adjusted-p-value of 0.05 (Benjamini-Hochberg procedure).

Moving average plots were generated using TCGA log₂ of RSEM transcript abundance quantification using 20 samples window. Spearman’s ρ computation and correlation tests were performed using R cor.test function.

QUANTIFICATION AND STATISTICAL ANALYSIS

Details of sample numbers, statistical analyses and significance are provided in the Figure legends.

Figure 1A: Error bars indicate S.D., n = 3, ** = p < 0.01.

Figure 1D: The moving average expression trendline for RRAGD or FNIP2 were made using a sample window size of n = 20. Spearman correlation coefficient was used to test for association between paired samples, RRAGD or FNIP2 with MITF.

Figure 1G–H: n = 3. The box and whisker plots show the median, two hinges corresponding to 25^th and 75^th percentiles and two whiskers extending 1.5× IQR. The black lines indicate means. For Pairwise comparisons between control (−Dox) and test (+Dox) all p = <0 .0001 calculated using edgeR glmQLFTest.

Figure 2A–C: The moving average expression trendline for TFE3 or TFEB were made using a sample window size of n = 20. Spearman correlation coefficient was used to test for association between paired samples, TFE3 or TFEB with MITF and TFE3 with TFEB. Figure 2I: Error bars indicate S.D. n = 3. * = p < 0.05; ** = p < 0.01; *** = p < 0.001; **** = p < 0.0001 ns = not significant, Student t-test.

No. of cells analyzed siCTRL DMEM (206,097, 200,000 and 200,000); siCTRL -Glucose (200,000, 200,000 and 200,000); siTFE3 DMEM (200,000, 200,000 and 125,305); and siTFE3 -Glucose (200,000, 200,000 and 52,884).

Figure 3C–J.

All peaks were called using the following fixed parameters.

# FDR rate threshold = 0.001000000.

# Fold over input = 4.00.

# Poisson p-value over input = 1.00e-04.

# Fold over local region = 4.00.

# Poisson p-value over local region = 1.00e-04 and variable parameters (modeled according to each dataset as these are sequencing depth dependent).

# FDR effective poisson threshold.

# FDR tag threshold.

Figure 3C: The box and whisker plots show the median, two hinges corresponding to 25^th and 75^th percentiles and two whiskers extending 1.5× IQR. The black lines indicate means.

Figure 3G: Boxes indicate interquartile range (25^th – 75^th percentile) and median (50^th percentile). The whiskers span minimum and maximum values within 1.5 × IQR. **** indicates p-value <0.001 One-way ANOVA.

Figure 3H: De novo motif identification using HOMER. The statistic accounts for multiple hypothesis testing across all candidate motifs. E-value = p-value×number of tested motifs. Where p-value denotes the probability that the observed enrichment (or greater) occurs by chance; the E-value denote the expected number of motifs with equal or better enrichment by chance.

Figure 3I: Enrichment is calculated using Homer cumulative hypergeometric test, plot as -LN(Enrichment). % overlapped were calculated from peaks overlapping the feature divided by total peaks called.

Figure 4A: Heatmap of normalized gene expression of differentially expressed genes between 501mel parental and HBSS treated (edgeR glmQLFTest, FC ≥ 2, p = <0.05).

Figure 4B: Volcano plot of differential gene expression (edgeR glmQLFTest, FC ≥ 2, p = <0.05).

Figure 4C: GSEA dot plots of pre-ranked values calculated from edgeR glmQLFTest −log10(p-value)/sign(logFC) using the cut-off adjusted-p-value of 0.05 (Benjamini-Hochberg procedure).

Figure 4F: Heatmap of normalized gene expression of differentially expressed genes between indicated pairs (edgeR glmQLFTest, FC ≥ 2, p = <0.05).

Figure 4G: Volcano plots of differential gene expression between indicated pairs (edgeR glmQLFTest, FC ≥ 2, p = <0.05).

Figure 4H: GSEA dot plots of pre-ranked values calculated from edgeR glmQLFTest −log10(p-value)/sign(logFC) of the indicated pair, using the cut-off adjusted-p-value of 0.05 (Benjamini-Hochberg procedure).

Figure 5C: Heatmap of normalized gene expression of differentially expressed genes between indicated samples relative to parental control (edgeR glmQLFTest, FC ≥ 2, p = <0.05).

Figure 5D–K: GSEA dot plots of pre-ranked values calculated from edgeR glmQLFTest −log10(p-value)/sign(logFC) of the indicated pair, using the cut-off adjusted-p-value of 0.05 (Benjamini-Hochberg procedure).

Figure 6A–D: For Seahorse assays, values were normalized to cell number. For the quantification and statistical analysis three or more independent repeats were performed for each experiment unless stated otherwise in the figure legend. The data were analyzed using SPSS statistical software for Microsoft Windows, release 6.0 (Professional Statistic, Chicago, IL, USA). For comparison of two groups two-tailed Student t-test was used. Mann Whitney-U test was used for comparison of two groups with nonnormal distribution. Values were considered significantly different when p < 0.05.

Figure 6A: Error bars = SD; n = 3.

Figure 6B–C: Error bars = SD; n = 3; ns = non-significant, * = p < 0.05, ** = p < 0.01 (black, comparison with parental cells; red, comparison with TKO); Student t-test.

Figure 6D: Numbers indicate % derived from each source. Error bars = SD; n = 3; ns = non-significant, * = p < 0.05, ** = p < 0.01, Student t-test comparing to parental WT B16-F10 cells.

Figure 6E–I Heatmap of normalized gene expression (CPM calculated using edgeR) at indicated genes.

Figure 7A–B: n = 5, bar show average and standard deviation of radiance. * = p < 0.05, ** = p < 0.01 using a two-tailed Student t-test.

Figure 7C: n = 5, * = p < 0.05; ** = p < 0.01; ns = not significant when compared to WT cells on the last day using a two-tailed Student t-test.

Figure 7D–E: n = 5, bar show average and standard deviation of radiance. ** = p < 0.001, ns = not significant using a two-tailed Student t-test.

Figure S2B, G: Spearman correlation coefficient was used to test for association and calculate Spearman’s p-value between paired samples for both replicates.

Figure S2E, H, J: Reference peaks were called using the same parameter as Figure 3C–J against indicated input.

Figure S3A: Heatmap of normalized gene expression of differentially expressed genes between indicated samples relative to parental control (edgeR glmQLFTest, FC ≥ 2, p = <0.05).

Figure 3B: Volcano plots of differential gene expression between indicated pairs (edgeR glmQLFTest, FC ≥ 2, p = <0.05).

Figure S3C–F: GSEA dot plots of pre-ranked values calculated from edgeR glmQLFTest −log10(p-value)/sign(logFC) of the indicated pair, using the cut-off adjusted-p-value of 0.05 (Benjamini-Hochberg procedure).

Figure S4A–H: GSEA dot plots of pre-ranked values calculated from edgeR glmQLFTest −log10(p-value)/sign(logFC) of the indicated pair, using the cut-off adjusted-p-value of 0.05 (Benjamini-Hochberg procedure).

Figure S4J: n = 12 (3 biological replicates in quadruplicate). Error bars indicate S.D. ** = p < 0.01; **** = p < 0.0001; ns = not significant when compared to WT cells at 96 h. Linear Mixed-Effects Models was used first to model the slopes then a post-hoc comparison using estimated marginal means accounting for two interacting factors (cell line and time) was used for pairwise comparison. The Bonferroni method was used to adjust p-values for multiple comparisons.

Figure S5A: Heatmap of normalized gene expression of differentially expressed genes between indicated samples relative to parental control (edgeR glmQLFTest, FC ≥ 2, p = <0.05) filtering out for genes that are commonly bound by all three factors.

Figure S5B: The p-value for each overlap is computed with the hypergeometric distribution (equivalently, one-tailed Fisher’s exact test), and the FDR q-value is obtained by applying the Benjamini–Hochberg (BH) multiple-testing procedure across all gene sets tested.

Figure S6B, G: Heatmap of normalized gene expression (CPM calculated using edgeR) at indicated genes.

Figure S6F: n = 3. Error bars = S.D. **** = p < 0.0001.

Supplementary Material

NIHMS2132725-supplement-1.pdf^{(5.9MB, pdf)}

NIHMS2132725-supplement-2.xlsx^{(11.6MB, xlsx)}

NIHMS2132725-supplement-3.xlsx^{(8.3MB, xlsx)}

NIHMS2132725-supplement-4.xlsx^{(297.9KB, xlsx)}

NIHMS2132725-supplement-5.xlsx^{(14.3KB, xlsx)}

SUPPLEMENTAL INFORMATION

Supplemental information can be found online at https://doi.org/10.1016/j.celrep.2025.116499.

Highlights.

MITF promotes mTORC1 activity in melanoma
Low glucose suppresses MITF and then TFEB to leave TFE3 to promote survival
MITF, TFEB, and TFE3 bind the same targets but regulate distinct gene expression programs
The MITF family differentially regulates tumor immune cell infiltration

ACKNOWLEDGMENTS

We thank Pati Faria-Shayler for technical support. This work was funded by the Ludwig Institute for Cancer Research (C.R.G., P.L., S.A., A.B., and L.M.); National Institutes of Health R01 CA268597-01 (C.R.G. and P.L.) and PO1 CA128814-06A1 (Y.V.G.); GABBA, University of Porto (D.D.); Fundacao de Amparo a Pesquisa do Estado de Sao Paulo (FAPESP) 2017/26148-5 (E.O.) and 2017/04926-6 (S.S.M.-E.); La Caixa (L.M.); the China Scholarship Council (L.L.); the American Cancer Society via the Holden Comprehensive Cancer Center (HCCC) (IRG-18-164-43); the Melanoma Research Alliance (ID#1426690) (C.K.); Fundación Robles Chillida (Caravaca, Murcia) (L.S.-d.-C.), Ministerio de Ciencia, Innovación y Universidades (MICIU) (project PID2023-149281OB-100 and FEDER, UE), and Fundación Séneca - Agencia de Ciencia y Tecnología de la Región de Murcia (FSRM/10.13039/100007801 (22544/PI/24, Spain) (J.N.R.-L.); Agencia Estatal de Investigación: MCIN/AEI/10.13039/501100011033 PID2023-151128OB-100 and PID2019-10487RB-100 (C. G.-J.) and PID2021-127645OA-100 (A.C.-C.); Comunidad de Madrid: PEJ-2021-AI/BMD-22973 and PEJ-2021-TL/BMD-21515 and PRECICOLON-CM, P2022/BMD-7212 (C.G.-J.); and Fundación Séneca, Región de Murcia (Spain) (21407/FPI/20) (R.M.-D.).

Footnotes

DECLARATION OF INTERESTS

The authors declare no competing interests.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

NIHMS2132725-supplement-1.pdf^{(5.9MB, pdf)}

NIHMS2132725-supplement-2.xlsx^{(11.6MB, xlsx)}

NIHMS2132725-supplement-3.xlsx^{(8.3MB, xlsx)}

NIHMS2132725-supplement-4.xlsx^{(297.9KB, xlsx)}

NIHMS2132725-supplement-5.xlsx^{(14.3KB, xlsx)}

Data Availability Statement

All ChIP-seq and RNA-seq datasets have been deposited at the Gene Expression Omnibus (GEO) and are publicly available as of the date of publication. Accession numbers are listed in the key resources table.
Original western blot images have been deposited at Mendeley at https://doi.org/10.17632/p6pr9tv6kv.1 and are publicly available as of the date of publication.
This paper does not report original code.
Any additional information required to reanalyze the data reported in this paper is available from the lead contact upon request.

KEY RESOURCES TABLE.

REAGENT or RESOURCE	SOURCE	IDENTIFIER
Antibodies
Mouse monoclonal anti-MITF (C5)	Abcam Cat# ab12039	RRID: AB_298801
Mouse monoclonal anti-GAPDH (6C5)	Santa Cruz Biotechnologies Cat# sc-32233	RRID: AB_627679
Mouse monoclonal anti-PTEN (2F4C9)	ThermoFisher Scientific Cat#32–5800	RRID: AB_2533087
S6	Cell Signaling Cat#2217	RRID: AB_331335
pS6	Cell Signaling Cat#2211	RRID: AB_331679
ACTIN (AC-40) mouse monoclonal	Sigma-Aldrich Cat# A4700	RRID: AB_2560935
RRAGD	Cell Signaling Cat# 4470	RRID: AB_1950380
FNIP2	Cell Signaling Cat#57612	RRID: AB_2799530
TFEB Hs	Cell Signaling Cat# 4240	RRID: AB_11220225
TFE3	Sigma-Aldrich Cat# HPA023881	RRID: AB_1857931
TFE3 (for Immunofluorescence)	Cell Signaling Cat#37785S	RRID: AB_2799119
TFEB Mm	Cell Signaling Cat# 83010	RRID: AB_3105800
DEPTOR	Novus Cat# NBP1–49674	RRID: AB_10011798
HA	Roche Cat#11666606001;	RRID: AB_514506
HA	Sigma-Aldrich Cat#H3663	RRID: AB_262051
HA	Cell Signaling Technology Cat#2367	RRID: AB_2314619
ERK 2	Santa Cruz Biotechnology Cat#sc-153	RRID: AB_2141293
ERK 2	Santa Cruz Biotechnology Cat#sc-1647	RRID: AB_627547
GAPDH	Santa Cruz Biotechnology Cat#sc-32233	RRID: AB_627679
F4/A80	Bio-Rad Cat# Cl A3–1	RRID: AB_394533
NKp46	Thermo Fisher Cat# PA5–79720	RRID: AB_2746835
CD8a	Abcam Cat# ab4055	RRID: AB_304247
Anti-Mouse Alexa Fluor 488	ThermoFisher Scientific Cat#A11001	RRID: AB_2534069
Anti-Mouse Alexa Fluor 546	ThermoFisher Scientific Cat#A10036	RRID: AB_11180613
Anti-Rabbit Alexa Fluor 488	ThermoFisher Scientific Cat#A11008	RRID: AB_143165
Anti-Rabbit Alexa Fluor 546	ThermoFisher Scientific Cat#A10040	RRID: AB_2534016
Alexa Fluor 488 donkey anti-rabbit IgG (H + L)	ThermoFisher Cat#A21206	RRID: AB_2535792
Bacterial and virus strains
Subcloning Efficiency DH5a bacteria	Invitrogen	Cat#18265–017
Chemicals, peptides, and recombinant proteins
Torin	Cayman Chemicals	Cat# 10997
Geneticin	Gibco	Cat# 10131027
Puromycin	Gibco	Cat# A1113802
Doxycycline	Sigma-Aldrich	Cat# D9891
DMSO	Sigma-Aldrich	Cat# D2650
cOmplete^™ ULTRA Protease Inhibitor Cocktail	Sigma-Aldrich	Cat# 5892988001
Glutamax DMEM media	GIBCO	Cat#31966021
RPMI 1640 media	GIBCO	Cat#21875–034
HBSS	GIBCO	Cat#21090–022
DMEM	GIBCO	Cat#11965
RPMI NO GLUCOSE	GIBCO	Cat#11879–020
DMEM no glucose	GIBCO	Cat#11966–025
Normal Fetal Bovine Serum	Sigma	Cat#F4135
Lipofectamine CRISPRMAX	ThermoFisher	Cat# CMAX00001
OptiMem	GIBCO	Cat#31985–062
FuGENE6	Promega	Cat#E2692
BSA	Sigma-Aldrich	Cat#A9647–100G
Lipofectamine RNAiMAX	ThermoFisher	Cat# 13778100
Sodium Azide	Sigma-Aldrich	Cat#S2002
Critical commercial assays
ECL detection kit	Amersham Biosciences	Cat# RPN2106
RNA extraction RNeasy Mini Kit	Qiagen	Cat#74106
MycoAlert^™ mycoplasma detection kit	Lonza	Cat#LT07–318
PureLink^™ Genomic DNA Mini Kit	Invitrogen	Cat# K182002
QIAprep Spin Miniprep Kit	QIAGEN	Cat# 27106
Deposited data
RNA-seq from MITF, TFEB and TFE3 B16-F10 KO cell lines	This paper	GSE270594
RNA-seq from 501mel cell lines	This paper	GSE277250
TFEB ChIP-seq	This paper	GSE270585
TFE3 ChIP-seq	This paper	GSE270590
shMITF RNA-seq	This paper	GSE255256
RNA-seq from inducible CTNNB1 in IGR37 cells	Louphrasitthiphol et al.¹⁰⁷	GSE136803
CUT&RUN in SKMEL28	Chang et al.⁷⁵	GSE273815
Experimental models: Cell lines
IGR37 Human melanoma cell line (female)	Obtained from Luisa Lanfrancone, IEO	Aubert et al.¹⁰⁸
IGR39 Human melanoma cell line (female)	Obtained from Luisa Lanfrancone, IEO	Aubert et al.¹⁰⁸
501mel Human melanoma cell line. This cell line by STR analysis is 624 mel (male) but is widely called 501mel cells in many publications from different labs.	Obtained from Ruth Halaban, Yale	Zakut et al.¹⁰⁹
A375M Human melanoma cell line (male)	Obtained from ATCC	Giard et al.¹¹⁰
SKMEL28 Human melanoma cell line (male)	Obtained from ATCC	Fogh et al.¹¹¹
SKMEL30 Human melanoma cell line (female)	Obtained from ATCC	Carey et al.¹¹²
HBL Human melanoma cell line (female)	Obtained from Ghanem Ghanem, Brussels	Ghanem et al.¹¹³
WM793 Human melanoma cell line (male)	Obtained from the ATCC	Herlyn et al.¹¹⁴
WM266.4 Human melanoma cell line (female)	Obtained from the ATCC	Westermark et al.¹¹⁵
WM115 Human melanoma cell line (female)	Obtained from the ATCC	Westermark et al.¹¹⁵
WM9 Human melanoma cell line (male)	Obtained from the ATCC	Herlyn et al.¹¹⁶
MeWo Human melanoma cell line (male)	Obtained from the ATCC	Cornil et al.¹¹⁷
CHL-1 Human melanoma cell line (female)	Obtained from the ATCC (Derived from RPMI 7932 cells)	Patent US5017478A
HT-29 Human Colon Cancer (female)	Obtained from the ATCC	Fogh and Trempe¹¹⁸
B16-F10-Luciferase mouse melanoma cell line (male)	Obtained from Luis Sanchez del Campo	Sanchez-Del-Campo et al.⁸¹
B16-F10-Luciferase MITF KO mouse melanoma cell line (male)	Obtained from Luis Sanchez del Campo	Sanchez-Del-Campo et al.⁸¹
Experimental Models: Mice
Female C57BL/6	Envigo (Barcelona, Spain)	N/A
Oligonucleotides
siRNA control	Qiagen	Cat# 1027281
siRNA MITF 1 5′-AAAGCAGTACCTTTCTACCAC	Sigma	Falletta et al.²⁵
siTFE3	Santa Cruz	Cat# sc-38507
TFEB sgRNA 5′-AGTACCTGTCCGAGACCTAT	ThermoFisher A35533 CRISPR775049_SGM	This paper
TFE3 sgRNA 5′-GGTACTGTTTCACCTGCTGC	ThermoFisher A35533 CRISPR381077_SGM	This paper
Tfeb sgRNA 5′-TGGACACGTACTGTCCACCT	ThermoFisher A35533 CRISPR557759_SGM	This paper
Tfe3 sgRNA 5′-AGCGTGTAGGGTTCTCGAGG	ThermoFisher A35533 CRISPR284202_SGM	This paper
DEPTOR qRT PCR Forward 5′-ACTCATGACAGTCCCTTCTGC	N/A	This paper
DEPTOR qRT PCR Reverse 5′-GACACGATCTTGATCTCCTTGCT	N/A	This paper
PTEN qRT PCR Forward 5′-TGGCACTGTTGTTTCACAAGAT	N/A	This paper
PTEN qRT PCR Reverse 5′-TAGCTGGCAGACCACAAACT	N/A	This paper
MITF qRT PCR Forward 5′-AGGTACTTTGATTCCCAAGTCA	N/A	This paper
MITF qRT PCR Reverse 5′-AAGTTCTTTTGCGCGTTGCT	N/A	This paper
Recombinant DNA
Plasmid Piggybac expression system	N/A	Magnusdottir et al.¹¹⁹
Software and algorithms
MetaQuant	N/A	Bunk et al.¹²⁰
ImageJ	https://imagej.nih.gov/ij/	N/A
Cutadapt	N/A	Martin¹²¹
STAR	N/A	Dobin et al.¹²²
Homer	N/A	Heinz et al.¹²³
EdgeR	N/A	Robinson et al.¹²⁴
TreeView	N/A	Saldanha et al.¹²⁵
javaGSEA v.4.3.2	N/A	Subramanian et al.¹²⁶
emmeans v1.10.7	cran.r-project.org	N/A
nlme v.3.1–166	cran.r-project.org	N/A
fastqc v.0.11.9	GitHub - s-andrews/FastQC	N/A
trim_galore v.0.6.5	N/A	Martin et al.¹²¹
clusterProfiler v4.6.2	Bioconductor	N/A
DOSE v3.24.2	Bioconductor	N/A
enrichplot v.1.18.4	Bioconductor	N/A
VennDiagram v.1.7.3	cran.r-project.org	N/A
ggplot2 v.3.4.4	cran.r-project.org	N/A
GraphPad Prism 7	https://www.graphpad.com	N/A
Perseus	N/A	Tyanova et al.¹²⁷

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PERMALINK

MITF, TFEB, and TFE3 drive distinct adaptive gene expression programs and immune infiltration in melanoma

Diogo Dias

Erica Oliveira

Román Martí-Díaz

Sarah Andrews

Ana Chocarro-Calvo

Alice Bellini

Laura Mosteo

Yurena Vivas García

Jagat Chauhan

Linxin Li

José Manuel García-Martinez

José Neptuno Rodriguez-López

Silvya Stuchi Maria-Engler

Colin Kenny

Javier Martínez-Useros

Custodia García-Jiménez

Luis Sanchez-del-Campo

Pakavarin Louphrasitthiphol

Colin R Goding

SUMMARY

In brief

Graphical Abstract

INTRODUCTION

RESULTS

MITF promotes mTORC1 signaling

Figure 1. Control of mTORC1 by MITF.

Differential regulation of TFEB and TFE3 by glucose limitation

Figure 2. Differential expression of MITF, TFEB, and TFE3 on glucose limitation.

MITF, TFE3, and TFEB bind the same genes

Figure 3. MITF family members bind a similar repertoire of genes.

MITF family members regulate different gene sets

Figure 4. Differential gene expression in TFEB, TFE3, and TFEB/TFE3 KO human melanoma cells.

Figure 5. TFE3 and TFEB are not equivalent in gene regulation.

MITF family genes differentially impact metabolism

Figure 6. Differential regulation of metabolism and differentiation by MITF, TFEB, and TFE3.

Differential regulation of key target genes by the MITF family

MITF, TFEB, and TFE3 control tumor growth and immune cell infiltration

Figure 7. Reduced tumor growth of B16-F10 MITF/TFE KO cells in vivo.

DISCUSSION

Limitations of the study

RESOURCE AVAILABILITY

Lead contact

Materials availability

Data and code availability

KEY RESOURCES TABLE.

STAR★METHODS

EXPERIMENTAL MODEL AND STUDY PARTICIPANT DETAILS

Human tumor samples and immunohistochemistry

Mice and mouse tumor immunohistochemistry

Cell lines

Bacterial strains

METHOD DETAILS

Cell fractionation

Cell cycle and DNA fragmentation analyses

Western blotting

Immunofluorescence

Proliferation/survival assays

Seahorse analysis

RNA-seq

ChIP-seq and CUT&RUN

Bioinformatics analyses

Data visualisation

QUANTIFICATION AND STATISTICAL ANALYSIS

Supplementary Material

Highlights.

ACKNOWLEDGMENTS

Footnotes

REFERENCES

Associated Data

Supplementary Materials

Data Availability Statement

KEY RESOURCES TABLE.

ACTIONS

PERMALINK

RESOURCES

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