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Mitotane activates ATF4/ATF3 axis triggering endoplasmic reticulum stress in adrenocortical carcinoma cells
Aurora Schiavon a, Laura Sabaª, Carlotta Evaristoª, Jessica Petitib,” (D, Ymera Pignochino ª,c, Giulio Ferrero ªD, Giorgia Giordano ”,”, Cristina Tucciarello ª,”, Soraya Puglisia”, Giuseppe Reimondo a®, Massimo Terzolo ª,1, Marco Lo Iacono a,*,1
a Department of Clinical and Biological Sciences, University of Turin, Turin, Italy
b Division of Advanced Materials Metrology and Life Sciences, Istituto Nazionale di Ricerca Metrologica (INRiM), Turin, Italy
” Candiolo Cancer Institute, FPO-IRCCS, Candiolo, Turin, Italy
d Department of Oncology, University of Turin, Turin, Italy
ARTICLE INFO
Keywords:
Adrenocortical carcinoma Mitotane
RNAseq
ATF4
ATF3
ER stress
ABSTRACT
Adrenocortical Carcinoma is a rare and aggressive endocrine malignancy, that arises from cells of one of the three cortical layers of the adrenal gland. Radical surgery is the only curative treatment, even if recurrence rates are high. Therapeutic options are limited, with mitotane as the cornerstone of medical therapy. Despite 50 years of clinical use, the mechanism of action of mitotane has not yet been fully established, possibly due to the drug’s susceptibility to interaction with confounding factors that reduce its biological activity. In the present study, we evaluated by RNAseq the effect of mitotane on gene expression in the H295R cell line, in an environment free of known confounding factors. Our approach allowed us to identify transcriptional deregulation of the ATF4/ATF3 axis, often involved in ER stress. These results were also validated by ddPCR in independent experiments. Mitotane-mediated ATF4 overexpression was also confirmed at the protein level. We observed how an incre- mental concentration of mitotane could deregulate main biological pathways. Further, we confirmed, both at RNAseq and ddPCR level, the mitotane-mediated downmodulation of genes such as STAR, CYP11A1, CYP21A2, and HSD3B2, highlighting its effect on steroid hormones biosynthesis. Through our approach, we identified biological pathways altered by mitotane in early response stages and with low drug concentrations. Some of these pathways could potentially be investigated in the future as functional biomarkers to monitor adrenocortical carcinoma treatment or as new pharmacological targets for this rare disease.
1. Introduction
Adrenocortical Carcinoma (ACC) is a rare and aggressive endocrine malignancy, with an estimated incidence of 0.5-2 new cases per million people per year [1] and an overall 5-year survival rate between 16 % and 47 %, highlighting the poor prognosis associated with this cancer [2-4]. ACC is usually sporadic, but it has a congenital (Beckwith-Wiedemann syndrome) and/or hereditary (Li-Fraumeni syndrome, MEN1, Gardner syndrome, Lynch syndrome) form [5]. ACC arises from cells of one of the three cortical layers of the adrenal gland and frequently causes an
increase in steroid hormone production. About 50 %-60 % of patients show clinical manifestations of steroid excess, such as Cushing’s Syn- drome, virilization, and Conn’s syndrome; frequently, mixed pheno- types are evident given that multiple steroids may be concomitantly secreted by the tumor [4,6-9]. The driver genes underlying the differ- ences between ACC and other types of adrenocortical tumors remain unknown [10]. Significant insight into ACC biology has been provided by the comprehensive pan-molecular characterizations of many ACC cases performed by the two consortia, ENS@T [11] and TCGA [12].
Up to now, a limited range of therapeutic options is available for ACC
* Corresponding authors. E-mail addresses: aurora.schiavon@unito.it (A. Schiavon), laura.saba@unito.it (L. Saba), carlotta.evaristo@unito.it (C. Evaristo), j.petiti@inrim.it (J. Petiti), ymera.pignochino@unito.it (Y. Pignochino), giulio.ferrero@unito.it (G. Ferrero), giorgia.giordano@unito.it (G. Giordano), cristina.tucciarello@unito.it (C. Tucciarello), soraya.puglisi@unito.it (S. Puglisi), giuseppe.reimondo@unito.it (G. Reimondo), massimo.terzolo@unito.it (M. Terzolo), marco.loiacono@unito. it (M. Lo Iacono).
1 M.L. and M.T. equally contributed to this manuscript.
https://doi.org/10.1016/j.biopha.2025.117917
patients, with radical surgery remaining the only curative treatment, even though recurrence is reported in up to 60 %-70 % of patients [4, 13-15]. Mitotane remains the cornerstone of medical therapy used either as monotherapy or in combination with classical cytostatic agents [16-18]. Mitotane is also increasingly used in the postoperative adju- vant setting [19]. Mitotane, also known as 1,1-(o,p-Dichlorodiphenyl)-2, 2-dichloroethane (o,p’-DDD), commercially marketed under the brand name Lysodren® (HRA Pharma Rare Diseases, Paris, France), is deriv- ative of the insecticide DDT, from which it was first isolated in 1940, and approved by the Food and Drug Administration in 1970 for ACC treat- ment [20,21]. Mitotane primarily exerts its pharmacological effects on the adrenal cortex, particularly in the zona fasciculata and zona retic- ularis, leading to cell damage and impairment of the steroidogenesis process [22].
Despite years of use in the clinic, the mechanism of action is still unclear. The pharmacological effect of mitotane is thought to depend on the inhibition of steroidogenesis, intracellular lipid accumulation, and endoplasmic reticulum (ER) stress induction, leading to cell death [23]. Mitotane has a massive effect on steroidogenesis, although it remains unclear whether it inhibits key enzymes (e.g., CYP11A1 or CYP11A2), steroidogenic regulatory genes (e.g., SREBF coding for the Sterol Reg- ulatory Element-Binding Transcription Factor) or both [24]. The inhi- bition of SOAT1 (Sterol-O-acyl transferase 1) and the deriving ER stress are possible key molecular pathways activated by mitotane [25]. Studies showed that mitotane-mediated apoptosis and necroptosis may also be induced by the blockage of mitochondrial respiratory chain complexes I and IV, as well as the disassembly of mitochondria-associated mem- branes [26,27].
A possible reason for our limited understanding of the mitotane mechanism of action is the high susceptibility of in vitro experiments to the presence of confounding factors. Intriguingly, mitotane cytotoxicity seems to vary between experiments from different research groups, even when carried out on the same cell lines [23]. In fact, in our previous work, we demonstrated that the presence of confounding factors in culture media (such as BSA, commercial serum, and patient serum) in- terferes with the pharmacological effect of mitotane [28]. Similar results have been observed in other studies by Hescot et al., in which they identified an inverse correlation between mitotane activity and lipo- protein concentration in the media [29].
Considering these data, mitotane resistance could be an artifact caused by the experimental conditions, resulting in an inaccurate eval- uation of the mitotane pharmacological effect. In this study, we aimed to evaluate the mitotane effect on gene expression and the regulation of biological pathways under experimental conditions free of known con- founding factors.
2. Materials and methods
2.1. Drugs and chemicals
Mitotane was dissolved in absolute ethanol in a 156 mM stock so- lution and stored at -20℃. The drug was provided by MedChemExpress LLC (Monmouth Junction, NJ 08852, USA).
2.2. Cell lines and culture conditions
H295R cells were kindly provided by Prof. S. Sigala (Department of Molecular and Translational Medicine, University of Brescia, 25123 Brescia, Italy) and cultured at 37℃ with 5 % CO2 according to American Type Culture Collection (ATCC) instructions. Media and supplements were purchased from Euroclone. H295R cells were grown in DMEM: F12 50:50 medium (Gibco: Thermo Fisher Scientific, Waltham, Massachu- setts, USA) supplemented with 2.5 % NuSerum (Corning, #355100, Thermo Fisher Scientific, Waltham, Massachusetts, USA), penicillin/ streptomycin (Gibco) and ITS Premix (Corning #354350). Of note, we used ITS instead of ITS+ , as the latter contains Bovine Serum Albumin
(BSA) which was one of the additive substances evaluated in our experimental conditions [28]. Unless indicated, all experiments were conducted in the absence of serum. Cell lines were periodically tested for mycoplasmas and authenticated by genetic profiling through poly- morphic short tandem repeat loci with the PowerPlex® 16 System (Promega, Madison, Wisconsin, USA) and Applied Biosystems 3130 genetic Analyzer (Thermo Fisher Scientific, Waltham, Massachusetts, USA).
2.3. RNA extraction
Total RNA was extracted from cell lines, either treated or not treated with mitotane. Genomic DNA contamination was removed by DNAse I treatment (Promega, Madison, Wisconsin, USA). RNA was then quanti- fied via NanoDrop (Thermo Fisher Scientific, Waltham, Massachusetts, USA) and stored at -80°C.
2.4. RNA sequencing
Libraries were generated from total RNA using the Illumina TruSeq Stranded Total RNA Library Prep Gold and Novaseq 6000 System (Illumina), following the Illumina standards procedure and kits. FastP tool with default setting was utilized for quality control and pre- processing of FASTQ files that are essential to provide clean data for downstream analysis [30]. Subsequently, quantification of transcript expressions was generated by the Salmon algorithm (Ver 1.9.0, specific parameters: - 1 A -p 40 -validate Mappings -seqBias -gcBias), which considers experimental attributes and biases, commonly observed in real RNAseq data, to perform inferences on transcript expressions [31]. Next, the filtered counts’ table was used as input to determine differential gene expression, performed using the program edgeR version 3.32.1 in R [32]. This procedure resulted in a gene list containing false discovery rate (FDR), p-value, and counts per million mapped reads (CPM). Genes with an absolute |log2FC= > 1 (which corresponds to at least a 100 %-fold change) and FDR < 0.05 were considered differentially expressed genes (DEGs). These DEGs were used for subsequent analysis.
2.5. Functional enrichment analysis
Using the resulting list of DEGs obtained by key experimental con- trasts, a functional analysis was performed using an enrichment tool included in the limma R Bioconductor package [33]. Goana and Kegga functions test for over-representation of Gene Ontology (GO) terms or KEGG pathways in one or more sets of genes, optionally adjusting for abundance or gene length bias [34]. The p-values returned by Goana and Kegga are unadjusted for multiple testing. The authors chose not to correct automatically for multiple testing because GO terms and KEGG pathways are often overlapping, so standard methods of p-value adjustment may be overly conservative. For this reason, the best results obtained in the enrichment analysis were tested with the Fry function that implements rotation gene set tests proposed by Wu et al. [35]. Pathways or GO terms were assumed to be enriched if the Fry test false discovery rate (FDR) was < 0.01. To visualize and investigate interesting enriched KEGG pathways, pathway gene annotation was performed using Pathview 3.19 [36]. In addition, to make inferences about the upstream regulator, responsible for transcriptional patterns specific to the most indicative biological contrasts, we utilized the QuaternaryProd R package. This algorithm computes the significance of upstream reg- ulators in the network by performing causal reasoning using the Qua- ternary Dot Product Scoring Statistic (Quaternary Statistic) [37].
2.6. cDNA synthesis and droplet digital PCR (ddPCR)
2 µg of total RNA was reverse-transcribed with random hexamer primers and MultiScribe Reverse transcriptase (High Capacity cDNA Archive Kit, Applied Biosystems: Thermo Fisher Scientific), according to
the manufacturer’s instructions. Different primers were designed to evaluate genes involved in the “Steroid hormone biosynthesis” pathway using Primer Express 2.0 (Thermo Fisher Scientific, Waltham, MA, USA). Primer efficiency was calculated for each transcript with RT-PCR (ABI Prism 7500 Sequence Detection System; Applied Biosystems) by using serial dilutions of cDNA of H295R. The specificity of each amplicon was evaluated by analyzing the respective melting curves (Supplementary file 1). In addition, to confirm the involvement of the ATF4 pathway we used “assays on demand” (Thermo Fisher Scientific, Waltham, Massa- chusetts, USA) for the following genes (Assay ID): ATF4 (Hs00909569_g1), CHAC1 (Hs00225520_m1), ASNS (Hs04186194_m1), PSAT1 (Hs00253548_m1) and ATF3 (Hs00231069_m1). Each sample was partitioned into ~20,000 droplets by a droplet generator (QX200™M Droplet Generator, Bio-Rad, Hercules, CA, USA), and each droplet was amplified by using ddPCR™M Supermix (for Probes or Evagreen) (Bio- Rad, Hercules, CA, USA), and the thermal cycling conditions suggested by the manufacturer. Custom primers were used at a final concentration of 100 nM, while the “assays on demand” were diluted following the manufacturer’s indications. After the amplification, each sample was then loaded onto the QX200TM Droplet Reader (Bio-Rad, Hercules, CA, USA), and ddPCR data were analyzed with QX Manager™M analysis software (version 2.0, Bio-Rad, Hercules, CA, USA). Each sample was analyzed at least in biological duplicates for all the concentrations of mitotane in the study. The target concentration in each sample was expressed and normalized as a percentage of gene/HRPT1 (Gene Ratio); the differential expression was evaluated relative to mitotane 0 uM (not treated cell line) and expressed as base 2 log of fold change (log2(Gene Ratio [conc x]/Gene Ratio [OuM])). Statistical analysis was performed using t-tests with a significance level of p < 0.05, comparing each mitotane concentration versus the untreated control.
2.7. Western blot and protein analysis
To isolate total protein content, cells were lysed on ice with RIPA buffer (Thermo Fisher Scientific, Waltham, Massachusetts, USA) sup- plemented with Halt™M Protease and Phosphatase Inhibitors Single-Use Cocktail (Thermo Fisher Scientific, Waltham, Massachusetts, USA), and cell debris was removed by centrifugation at 16,000 x g at 4℃ for 10 min. Evaluation of ATF4 expression was performed by Western Blot analysis. Equal amounts of protein (40 µg/well) for each sample were loaded and analyzed through SDS-PAGE (Any kD™ Mini-PROTEAN® TGXTM Precast Protein Gels, Bio-Rad). Proteins were then transferred onto PVDF membranes (Trans-Blot Turbo Mini 0.2 um PVDF Transfer Packs, Bio-Rad) using Trans-Blot Turbo Transfer System and protocol recommended by the producer, blocked in EveryBlot Blocking Buffer (Bio-Rad) for 10 minutes and incubated overnight at 4℃ with primary antibodies (1:1000 dilution in TBS-Tween20 0.3 %) against ATF4 (Cell Signaling Technology (CST), # 11815, Danvers, MA, USA) and Vinculin (CST, # 13901). Anti-rabbit IgG/HRP (CST, #7074) and anti-mouse IgG/HRP (CST, #7076) were used as secondary antibodies (1:8000 dilution). Enhanced chemiluminescence method (Clarity ECL, Bio-rad, Hercules, CA, USA) was used for protein-bound detection and the im- ages were acquired using the ChemiDoc™ system (Bio-rad, Hercules, CA, USA).
3. Results
We have recently demonstrated that the presence of serum or BSA almost completely inhibits the biological activity of mitotane [28]. To elucidate, at the transcriptional level, the biological mechanisms influ- enced by mitotane treatment in an environment free from confounding factors, we treated the H295R cell line for 24 hours (h) with increasing concentrations of mitotane (0, 5, 10, 15, and 20 uM, respectively). For each mitotane concentration, we identified the genes differentially
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expressed compared to untreated H295R (NT). The volcano plots in Fig. 1A show the results of this analysis. As expected, the number of differentially expressed genes (DEGs) rose with increasing mitotane concentration, from a few to a few dozen for the first two concentrations and to thousands at the highest ones (Fig. 1A, dark red dots). Focusing on gene regulation, we observed that the number of upmodulated genes (red dots) was approximately the same as that of the downmodulated ones (Fig. 1A, dark blue dots) for each of the experimental contrasts considered. In Fig. 1B, we can observe the overlaps of the identified DEGs between the different mitotane concentrations, both as a general overview (Fig. 1B, green graph) and by dividing them between upre- gulated and downregulated genes (Fig. 1B, red and blue graphs, respectively). Again, the proportion of upmodulated and down- modulated genes appeared to be the same for each intersection.
There was a direct trend between the number of differentially expressed genes and the mitotane concentration used over 24 h (Pear- son’s correlation = 0.94; p-value = 0.058). This could indicate that certain genes are progressively regulated by increasing mitotane con- centrations. To evaluate this, we performed a multivariate analysis across all mitotane concentrations to identify genes that were either
upregulated or downregulated. We assessed two different scenarios: in the first, the untreated cells were used as a baseline (“normalizer”) and the gene expression trend was evaluated across the experimental con- trasts (5 uM/NT → 10 uM/NT → 15 uM/NT → 20 uM/NT). The top 300 genes identified with this approach are depicted in Fig. 2A and B, showing a notable divergence in gene expression starting at 10 uM mitotane compared to untreated cells, with maximal regulation observed at the highest concentrations (Fig. 2A and B, Supplementary file 2).
In the second approach, shown in Fig. 2C and D, we used the previous mitotane concentration as the “normalizer” of each experimental contrast (5 μΜ/ΝΤ → 10 μΜ/5 μΜ → 15 μΜ/10 μΜ → 20 μΜ/15 μΜ). This analysis was useful for identifying when gene expression changes peaked or plateaued. Interestingly, many genes showed maximum expression levels at 15 uM mitotane after 24 h, which then either sta- bilized or decreased at 20 uM (Fig. 2C and D). A detailed list of DEGs and their transcriptional regulations for each comparison is provided in the supplementary data (Supplementary file 2).
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3.1. GO and KEGG enrichment analysis
Fig. 3 provides an overview of the Gene Ontology (GO) analysis performed on the DEGs identified in the previous analyses, as tabulated in Supplementary file 3. A word cloud highlights the most frequent GO terms, including “cellular component”, “cellular anatomical entity” and “extracellular region” (Fig. 3A), which were enriched across most experimental contrasts. Fig. 3B shows the top 20 enriched terms iden- tified in the multivariate analysis, illustrating the course of gene regu- lation (Fig. 3B, bubble plot). In the lower panels, we highlighted the top 20 GO enrichments for experimental contrasts indicative of mitotane- mediated gene expression: the point at which its transcriptional effect first became evident, 10 uM for 24 h (Fig. 3C, bubble plot), and the concentration with the highest number of deregulated genes (mitotane 20 µM for 24 h) (Fig. 3D, bubble plot).
Since the same gene can be associated with multiple GO terms, the p- values of the initial analysis were not adjusted for multiple testing. To overcome this, we performed a more stringent enrichment analysis using the FRY test, which considers unique genes. This test, combined with FDR correction, also indicated the predominant direction of the pathway regulation (UP or DOWN modulated). The trend of gene expression across the concentration gradient was enriched with terms such as “response to stimuli”, “chemical agents” and “regulation of biological processes”, such as “cell migration” or “developmental processes”. Noteworthy was the difference in GO term enrichment between 10 and 20 µM. In the first case (10 uM), metabolic processes, including “sterol biosynthesis”, “cholesterol”, “lipids” and/or “steroids”, seemed to be turned off (for all analyses, FRY test FDR << 0.01 trend DOWN). In the second case (20 uM), metabolic processes shift towards reduced activity in favor of pathways like “nucleotide biosynthesis”, including “phosphate nucleotides”, while GO terms became more focused on “cell membranes” and “mitochondrial”. In particular, the mitochondrion appeared to be a key target, with downregulated activities related to the “mitochondrial membrane”, including “ATP synthesis coupled to electron transport”, “aerobic respiration” and “oxidative phosphorylation” (all indicated terms were predominantly downmodulated with FRY test FDR << 0.01).
The same analysis was performed to observe the enrichment of the metabolic pathways included in the KEGG database (Fig. 4 and tabu- lated in Supplementary file 4). The most prominent pathways in the analysis were “Biosynthesis of amino acids”, “Metabolic pathways”, and “Glutathione metabolism”, which we observed both in the multivariate analysis and in the different experimental contrasts (Fig. 4A and B). The metabolic pathways enriched in the KEGG database confirmed what was observed in the previous paragraph of the Gene Ontology. The gene trends focused on “lipids”, “atherosclerosis”, and various syntheses, including that of “steroids”, the “metabolism of mannose”, “glutathione” and “xenobiotics”. Interestingly, mitotane downmodulated the path- ways involved in chemical carcinogenesis, in particular, the “production of adducts”, in “ferroptosis” and “cellular senescence”, were observed as significant also at mitotane concentrations of 15 and 20 uM. Even in the case of specific contrasts at 10 and 20 uM (Fig. 4C and D, respectively), we observed a behavior similar to the GO analysis. In particular, at the lowest concentration, we observed a significant downmodulation of pathways involving the synthesis of “terpenoids”, which are precursors of sterols, and the metabolism and excretion of “fats and fatty acids”, synthesis of “cortisol”, “aldosterone”, and “steroid hormones” (all indi- cated pathways were predominantly downmodulated with FRY test FDR << 0.01). At the highest concentration, we still observed the down- modulation of “steroid synthesis”, but the majority of pathways seemed to converge on the inhibition of “oxidative phosphorylation” and “chemical carcinogenesis”, such as the production of “reactive oxygen species/DNA adducts” and processes related to “oxidative phosphory- lation” such as “thermogenesis” (all predominantly downmodulated with FRY test FDR << 0.01). GO/KEGG full tables and some represen- tative specific pathways were included in Supplementary file 3, 4, and 5.
Transcriptional analysis provides us with information about the
genes activated by one or more regulators and transcription factors, without telling us which ones are responsible for the observed pattern. To predict the putative upstream regulators affected by mitotane, we used the R package QuaternaryProd. This algorithm, for a given set of differentially expressed genes, calculates the significance of the up- stream regulators in the network using the quaternary dot product score statistic. We analyzed all genes upregulated by the contrast at 10 uM with this QuaternaryProd and observed that the most significant up- stream regulators were the genes ATF4 and MAPK8 (both p-value << 0.01). To confirm this result, we examined genes indicated in the literature as targets of Activating transcription factor 4 (ATF4) [38-43], including CHAC1, ASNS, PSAT1, and ATF3. All these genes were upmodulated in our analysis, consistent with ATF4 activity. Among these, CHAC1, ASNS, and PSAT1 were early mitotane responders, exhibiting upmodulation at all the concentrations used in our analysis (CHAC1 log2(FC) 2-4, FDR << 0.01; ASNS log2(FC) 1.7-3.1, FDR < < 0.01; PSAT1 log2(FC) 1.2-2.2, FDR < 0.02). In contrast, ATF3 was not significantly regulated at the lowest concentration, but it was the most upmodulated gene observed in both the highest mitotane con- centrations, with the highest upregulation trend (15 uM log2(FC) 4.48 FDR << 0.01, 20 uM log2(FC) 5.35 FDR << 0.01 and trend FDR < < 0.01).
Mitotane likely deregulates a plethora of transcription factors. In an attempt to reduce the complexity of the DEGs model and to identify specific regulators, we ran QuaternaryProd with a subset of the DEGs. In particular, we selected genes present in the specific KEGG pathways enriched in our analysis at mitotane 20 uM, a concentration with the highest number of deregulated genes in KEGG pathways. To limit biases due to transcriptional pattern sub-selections, we reported here the genes that were present only in multiple KEGG metabolic pathways. Notably, we observed that POMC (Pro-opiomelanocortin) could be a good up- stream regulator candidate for the KEGG pathways “Steroid hormone biosynthesis”, “Aldosterone synthesis and secretion”, and “Cortisol synthesis and secretion”. In contrast, members of the SREBF family were significantly suppressed for KEGG pathways, specifically “Biosynthesis of unsaturated fatty acids” and “Fatty acid metabolism” for SREBF1, and “Steroid biosynthesis” and “Terpenoid backbone biosynthesis” for SREBF2.
To confirm this data, we repeated the experiments in H295R, per- forming at least in biological duplicates, and we re-evaluated the tran- scriptional pattern of “Steroid hormone biosynthesis” and ATF4 pathway using ddPCR technology. Fig. 5 shows the results for six key genes involved in the “steroid hormone biosynthesis” pathway, where RNAseq analysis showed a clear down-modulation. Except CCN3, which exhibited significant downmodulation only at 10 uM, the remaining genes appeared to correlate almost perfectly with the RNAseq analysis results. Almost all transcripts were halved already at 5 uM, and many of these reached their maximum shutdown at 15 or 20 uM. In particular, for CYP21A2, CYP17A1, and STAR we could appreciate the coupled trend between the two analyses, which trace the same pattern of gene expression (Fig. 5).
Similar results were observed for ATF4 pathway (Fig. 6A). Indeed, the regulations observed in ddPCR were consistent with RNAseq anal- ysis for the AFT4 pathway genes deregulated in response to mitotane. In particular, the early activation of ASNS, CHAC1, and PSAT1 genes was evident from the significant rapid up-modulation already at 5 uM, with intensities even higher than those observed in the omics analysis (for all p < 0.01). Furthermore, we evaluated the transcriptional activation of ATF4 (Fig. 6B). Although mitotane treatment resulted in ATF4 over- expression, the statistical significance and intensity of this regulation at the transcript levels were near the threshold limits of our analysis. This finding aligns with the RNAseq results. In contrast, at the protein level, we observed a clear modulation of the ATF4 in response to mitotane treatment. Indeed, the levels of this protein, undetectable in the un- treated H295R cell line, became evident with increasing concentration of the drug (Fig. 6B). This upmodulation of the ATF4 protein further
A
B
TOP 20 GO multi
system development -
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☒
signaling -
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response to stimulus -
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☒
secondary alcohol metabolic process
response to organic substance -
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secondary alcohol biosynthetic process developmental process plasma membrane
response to chemical -
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response to endoplasmic reticulum stress small molecule metabolic process
regulation of multicellular organismal process -
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alcohol metabolic process organic acid metabolic process cell migration small molecule biosynthetic process cellular amino acid metabolic process multicellular organismal process cytoplasm cellular response to stimulus
regulation of biological process -
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☒
multicellular organismal process -
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multicellular organism development -
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☒
cholesterol biosynthetic process
extracellular region cellular_component multicellular organism development
oxoacid metabolic process
Term
molecular_function -
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☒
membrane
molecular_function
lipid biosynthetic process
cell periphery
regulation of biological quality
extracellular space
carboxylic acid metabolic process
developmental process -
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☒
☒
signaling cell’communication
sterol biosynthetic process
☒
☒
o steroid metabolic process
cellular_component -
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steroid biosynthetic process
cellular response to stimulus -
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☒
sterol metabolic process
cellular anatomical entity
cellular response to chemical stimulus -
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☒
cholesterol metabolic process
cellular anatomical entity
☒
☒
☒
cell periphery -
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☒
☒
cell migration -
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☒
☒ C
cell communication -
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☒
O ☒
biological_process -
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☒
☒
anatomical structure development -
☒
☒
☒
-20
-15
-10
-5
0
log10(P.DE)
☒ 5µM
☒ 10μΜ
☒ 15μΜ
☒ 20μ.Μ
☒ multi
☐ Up modulated
☐ Down modulated
☐ multi
C
TOP 20 GO 10uM vs NT
D
TOP 20 GO 20M vs NT
tRNA aminoacylation for protein translation -
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small molecule metabolic process -
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☒
tRNA aminoacylation -
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oxidative phosphorylation -
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sterol metabolic process -
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☒
organelle inner membrane -
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☒
sterol biosynthetic process -
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☒
nucleotide metabolic process -
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☒
steroid metabolic process -
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O ☒
nucleotide biosynthetic process -
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steroid biosynthetic process -
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nucleoside phosphate metabolic process -
secondary alcohol metabolic process -
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nucleoside phosphate biosynthetic process -
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secondary alcohol biosynthetic process -
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nucleobase-containing small molecule metabolic process -
oxoacid metabolic process -
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mitochondrion -
Term
organic hydroxy compound metabolic process -
☒
Term
mitochondrial membrane -
organic acid metabolic process -
☒
☒
mitochondrial inner membrane -
lipid metabolic process -
O
☒
mitochondrial envelope -
lipid biosynthetic process -
☒ ☒
mitochondrial ATP synthesis coupled electron transport -
ligase activity, forming carbon-oxygen bonds -
☒
inner mitochondrial membrane protein complex -
cholesterol metabolic process-
☒
☒ O
extracellular region - ☒
☒
cholesterol biosynthetic process -
☒
☒
cellular_component -
☒
☒
carboxylic acid metabolic process -
☒
☒
cellular respiration -
☒
☒
aminoacyl-tRNA ligase activity -
☒
☒
cellular anatomical entity -
☒
☒
amino acid activation -
☒
☒
ATP synthesis coupled electron transport -
☒
alcohol metabolic process -
☒
☒
aerobic respiration -
☒
-15
-10
5
0
-20
-15
-10
.5
0
log10(P.DE.vs10)
log10(P.DE.vs20)
TOP 20 KEGG multi
A
B
Steroid biosynthesis
☒
☒
Phenylalanine metabolism -
☒
Neurotrophin signaling pathway
Pathways in cancer -
Biosynthesis of unsaturated fatty acids
Glycolysis / Gluconeogenesis
ECM-receptor interaction Inflammatory mediator regulation of TRP channels
Oocyte meiosis .
☒
☒
Central carbon metabolism in cancer Fatty acid metabolism
Non-small cell lung cancer -
☒
Terpenoid backbone biosynthesis
Metabolism of xenobiotics by cytochrome P450 -
Fluid shear stress and atherosclerosis DNA replication
Lipid and atherosclerosis -
p53 signaling pathway Steroid biosynthesis Protein export
Aldosterone synthesis and secretion
Glutathione metabolism -
0
Cytokine-cytokine receptor interaction
Progesterone-mediated oocyte maturation Drug metabolism - other enzymes
Fructose and mannose metabolism -
☒
AMPK signaling pathway
Chemical carcinogenesis - DNA adducts
FoxO signaling pathway -
☒ ☒
Apoptosis
PPAR signaling pathway Oocyte meiosis
Cell cycle Pertussis
MicroRNAs in cancer
Hepatocellular carcinoma Metabolic pathways Biosynthesis of amino acids
Biosynthesis of cofactors
Cortisol synthesis and secretions Cysteine and methionine metabolism
Fluid shear stress and atherosclerosis -
☒
☒
o
Ferroptosis -
☒
o ☒
Cellular senescence Ferroptosis Cytoskeleton in muscle cells
Platinum drug resistance
Cushing syndrome
Cytoskeleton in muscle cells -
☒
☒
Fat digestion and absorption
Fatty acid biosynthesis
Pathways in cancer
Cytokine-cytokine receptor interaction -
☒ ☒
Proteoglycans in cancer
Cholesterol metabolism
Chemical carcinogenesis - DNA adducts -
☒
☒
Carbon metabolism
Pyruvate metabolism
Ovarian steroidogenesis
Cellular senescence -
☒
☒
☒
Oxytocin signaling pathway
Glutathione metabolism
Cell cycle -
☒
☒
Drug metabolism - cytochrome P450
Biosynthesis of amino acids -
☒
☒
☒
Protein processing in endoplasmic reticulum Chemical carcinogenesis - receptor activation Metabolism of xenobiotics by cytochrome P450
AMPK signaling pathway -
☒
☒
-8
-6
-4
-2
0
log10(P.DE)
Metabolic pathways - ☒
O ☒
-20
10
0
log10(P.DE)
☒ 5HM
☒ 10μM
☒ 15μM
☒ 20μ.Μ
☒ multi
☐ Up modulated
☐ Down modulated ☐ multi
C
TOP 20 KEGG 10uM vs NT
D
TOP 20 KEGG 20uM vs NT
Transcriptional misregulation in cancer -
☒
☒
Thermogenesis -
☒
Terpenoid backbone biosynthesis -
☒
☒
☒
Steroid biosynthesis -
Steroid hormone biosynthesis -
☒
☒
Retrograde endocannabinoid signaling -
☒
Steroid biosynthesis -
☒
☒
☒
Protein processing in endoplasmic reticulum -
☒
Staphylococcus aureus infection -
☒
☒
Prion disease -
☒
☒
Pyruvate metabolism -
☒
☒
Pathways of neurodegeneration - multiple diseases -
☒
☒
Ovarian steroidogenesis -
☒
☒
☒
Parkinson disease -
☒
☒
☒
Oxidative phosphorylation -
☒
One carbon pool by folate -
☒
Pathway
Glyoxylate and dicarboxylate metabolism -
☒
☒
Non-alcoholic fatty liver disease -
Pathway
☒
O ☒
Fatty acid metabolism -
☒
☒
Metabolic pathways -
☒
Huntington disease -
☒
Fat digestion and absorption -
☒
Fat digestion and absorption -
☒
O
Cytokine-cytokine receptor interaction -
☒
☒
DNA replication -
☒
Cushing syndrome -
☒
☒
Diabetic cardiomyopathy -
☒
Cortisol synthesis and secretion -
☒ ☒
☒
Chemical carcinogenesis - reactive oxygen species -
☒
☒
Biosynthesis of unsaturated fatty acids -
☒
☒
Chemical carcinogenesis - DNA adducts -
☒
Biosynthesis of amino acids -
☒
☒
Cell cycle -
☒
Aminoacyl-tRNA biosynthesis -
☒
☒
Carbon metabolism -
☒
☒
Aldosterone synthesis and secretion -
☒
☒
Amyotrophic lateral sclerosis -
☒
☒
Alcoholic liver disease -
O
☒ ☒
·
Alzheimer disease -
☒
☒
-8
-6
.4
-2
0
-20
-10
0
log10(P.DE.vs10)
log10(P.DE.vs20)
Metabolic pathways -
☒
O ☒ o
-20
10
0
log10(P.DE.vs10)
CCN3
CYP11A1
CYP17A1
2
0
0
**
*
**
*
1
-1
-1
Log2(FC)
0
-2
-2
-1
İ
-3
-2
-3
-4
0
5
10
15
20
0
5
10
15
20
0
5
10
15
20
Mitotane [μM]
Mitotane [uM]
Mitotane [[M]
CYP21A2
HSD3B2
STAR
0
2
0
**
**
**
*
**
**
I
İ
I
0
Log_(FC)
-1
-1
·
-2
İ
-2
-2
-4
0
5
10
15
20
0
5
10
15
20
0
5
10
15
20
Mitotane [uM]
Mitotane [M]
Mitotane [uM]
supports the involvement of this pathway in the biological activity of mitotane.
4. Discussion
A controlled environment, free from confounding factors, is essential to study the pharmacological effect of any drug. Under such conditions, it becomes possible to identify the primary targets early and expand the knowledge of the active compounds (or molecules) acting on them. On these bases, we evaluated the transcriptional pattern of mitotane in the H295R cell line, in the absence of BSA and serum, as both interact with mitotane and block its biological activity [28]. This approach allowed us to observe a remarkable genetic deregulation with exposure to mitotane concentrations ten times lower than those reported in the current literature, within a 24 h exposure interval. Consequently, we observed initial deregulations even at lower mitotane concentrations, with a clear distinction from untreated cells, particularly at 10 uM after 24 h.
Interestingly, we did not observe significant differences in the di- rection (up or down) of gene deregulation across the various experi- mental contrasts analyzed, whether evaluating the contrasts individually or examining the transcriptional trends. Indeed, across the different mitotane concentrations, we observed a rather good symmetry between the genes that were up and downmodulated after 24 h. How- ever, when analyzing the enrichment of KEGG pathways or GO terms, we noticed that most of the enriched pathways were characterized by a predominant gene downmodulation, with only a few upmodulated genes, often detected at the highest concentrations tested. Our analysis indicated that mitotane was responsible for the systematic suppression of several pathways crucial for the adrenal cortex cells. A key target appeared to be the mitochondrial processes, starting with specific
pathways, such as those responsible for steroid hormone synthesis and related genes. This suppression extended to the deregulation of path- ways essential for oxidative respiration, ultimately impairing the pri- mary functions of the mitochondrion itself.
Our analysis confirmed, at both RNAseq and ddPCR levels, the known mitotane-mediated downmodulation of different genes such as STAR, CYP11A1, CYP21A2, and HSD3B2 [26,27,44-46], even after just 24 h. Intriguingly, although a significant regulatory trend was evident, marked downregulation was only observed at the highest concentrations tested. The deregulation of CYP11B1 isoforms appears to be more complex, with literature data reporting controversial findings. Depending on the experimental conditions, CYP11B1 has been observed as either downmodulated [27,47,48] or upmodulated following mito- tane treatment [46]. In contrast, the CYP11B2 gene is consistently re- ported to be transcriptionally inhibited by mitotane in vitro [27]. In our analysis, CYP11B1 was not deregulated under any of our experimental conditions, and no trend was observed. Similarly, CYP11B2 was not significantly deregulated at any tested concentrations, though we observed a trend in gene regulation. This trend ranged from upregula- tion at the two lowest concentrations to downregulation at the two highest concentrations. The behavior of all these genes implicated in steroid hormone biosynthesis suggests that their deregulation might not occur immediately, but may require a priming process to reinforce their downmodulation over time. In this scenario, understanding the primary drivers of this transcriptional regulation could be crucial for identifying druggable metabolic pathways and developing new drugs.
We identified POMC as a specific upstream modulator. Previous studies have found that mitotane reduces POMC expression and blocks the stimulatory effects of corticotropin-releasing hormone on pituitary cell viability [49]. In our analysis, we did not observe deregulations of
A
B
ASNS
CHAC1
4
*
5
:
ATF4
3
4
.
2.0
*
*
*
Log_(FC)
3
2
1.5
2
1
1.0
1
0.5
0
5
10
15
20
0
5
10
15
20
Mitotane [μΜ]
Mitotane [uM]
0
5
10
15
20
PSAT1
ATF3
4
*
6
**
*
**
.
3
Mitotane [ ]
4
Kd
M
ΟμΜ
10μΜ
20μ.Μ
135
2
Vinculin
100
2
63
ATF4
1
48
0
5
10
15
20
0
5
10
15
20
Mitotane [uM]
Mitotane [uM]
this gene, although mitotane appeared to act as a repressor of this pathway in ACC cells. Furthermore, we observed that other possible upstream regulators could be the SREB family genes. In our experiment, both SREB1 and SREB2 genes were specifically downmodulated, along with the pathways dependent on them. Our findings are in line with those previously reported by Sbiera et al., who identified “SCAP/SREBF transcriptional control of cholesterol and fatty acids biosynthesis” as one of the main mitotane-deregulated pathways regulated by the SREB family [25]. Interestingly, the same study also highlighted SOAT1 as a key molecular target of mitotane, whose inhibition induces ER stress and could serve as a predictive marker of drug response [25,50]. In contrast, our analysis found that SOAT1 was not an early responsive gene to mitotane treatment. Significant downmodulation was observed only at the highest concentration, with a mere trend at lower doses. We hy- pothesize that this lack of strict dependence on mitotane treatment could explain the failure of SOAT assessment to predict mitotane response in patients with ACC in subsequent studies [51,52].
Our analysis also supports the hypothesis that mitotane induces ER stress, although our data suggest new key targets. Increased ER stress and unfolded protein response (UPR) indicated changes in the cellular environment, often due to disturbances in cellular metabolism. In our analysis, we observed the activation of several key transcription factors involved in the UPR, including ATF4, ATF6, and XBP1 [40], with varying degrees of significance. In our work, mitotane appeared to mediate this process by triggering a cell death cascade event that
involved the ER, mitochondrial membrane, and oxidative stress. As early as 5 uM for 24 h, we observed the upmodulation of CHAC1, ASNS, and PSAT1 in both analyses, and these genes were also significantly upmo- dulated at all higher concentrations tested. In addition, ATF3 was the most upmodulated gene, particularly at the highest mitotane concen- trations, showing the strongest upregulation trend. This genetic behavior, as suggested by our inferential and protein analysis, appeared to affect the signaling axis involving the transcriptional factor ATF4. This gene, functioning both as a transcriptional activator and inhibitor, can be upregulated in response to various types of cell stress, including ER stress, oxidative stress, amino acid depletion, and integrated stress response [53]. Its role in regulating these genes is well-established [38-43]. In models found in the literature, ATF4 induces expression of ATF3, followed, at least in the case of amino acid deprivation, by ATF3 enhancing CHOP expression [38]. Literature data also suggest that CHAC1 is an important downstream target of the ATF4-ATF3-CHOP pathway, promoting apoptosis in response to oxidized phospholipids [40]. While the role of ATF4 in inducing ferroptosis is currently debated, it seems to promote it through the activation of NUPR1 and inhibit it via the pathway involving CHAC1 (reviewed in Tang et al.l. [53]). In our study, mitotane upmodulated both these genes, even though the drug has not been previously found to cause ferroptosis [54,55]. Further- more, we observed a clear modulation of ATF4 protein levels in response to mitotane treatment. This finding confirms the transcriptional cascade observed in our RNAseq analysis and highlights how this pathway could
be important for the biological effect of mitotane. Further studies will be needed to define how modulation of ATF4 and its effectors might in- fluence mitotane-mediated insult in ACC cells. The necessity to further investigate this molecular pathway is also underlined by a very recent study that identified the ATF family, particularly the high protein expression of ATF4, as an unfavorable prognostic marker in ACC (pre-print under review https://doi.org/10.21203/rs.3.rs-4278365/v1).
In conclusion, to the best of our knowledge, this is the first study to evaluate the transcriptional deregulation induced by mitotane in H295R cells in the absence of confounding factors that could alter its biological activity. Through our approach, we identified the main pathways that are deregulated by mitotane as early as 24 h post-treatment. While some of these pathways have been previously linked to mitotane treatment with mixed success, even at extreme mitotane concentrations, we identify the ATF4 pathway as one of the first molecular pathways trig- gered by mitotane, both at the protein and transcriptional levels. Some of these effectors could potentially be exploited in the future to identify functional biomarkers for treatment monitoring or as new pharmaco- logical targets for ACC.
Funding
This work was supported by Associazione Italiana per la Ricerca sul Cancro (AIRC), grant number IG2019-23069 to Massimo Terzolo.
CRediT authorship contribution statement
Pignochino Ymera: Methodology, Formal analysis. Giordano Giorgia: Methodology, Data curation. Ferrero Giulio: Formal analysis. Puglisi Soraya: Data curation. Tucciarello Cristina: Methodology, Data curation. Terzolo Massimo: Writing - original draft, Supervision, Funding acquisition, Conceptualization. Reimondo Giuseppe: Data curation. Saba Laura: Writing - original draft, Methodology, Investi- gation. Schiavon Aurora: Writing - original draft, Methodology, Investigation, Conceptualization. Lo Iacono Marco: Writing - original draft, Supervision, Formal analysis, Data curation, Conceptualization. Petiti Jessica: Writing - original draft, Methodology, Investigation, Conceptualization. Evaristo Carlotta: Methodology.
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Appendix A. Supporting information
Supplementary data associated with this article can be found in the online version at doi:10.1016/j.biopha.2025.117917.
Data availability
Data will be made available on request.
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