Targeting PKLR and lipogenic enzymes through JNK inhibition to develop a therapeutic strategy for MASLD and MASH

Targeting PKLR and lipogenic enzymes through JNK inhibition to develop a therapeutic strategy for MASLD and MASH | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Targeting PKLR and lipogenic enzymes through JNK inhibition to develop a therapeutic strategy for MASLD and MASH Adil Mardinoglu, Woonghee Kim, Mengzhen Li, Xinmeng Liao, Sevilay Özmen, and 17 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7114368/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract Pyruvate kinase liver and red blood cells (PKLR) has emerged as a key gene associated with metabolic dysfunction-associated steatotic liver disease (MASLD). Through a computational drug repurposing approach, we identified JNK-IN-5A as a small molecule that significantly inhibits the c-Jun N-terminal kinase (JNK) family and suppresses PKL expression in HepG2 cells. In this study, we further evaluated JNK-IN-5A and its derivatives, including SET-151, SET-152, SET-162, and SET-130, as potential therapeutic candidates for MASLD. Building on our previously established HepG2 de novo lipogenesis (DNL) steatosis model, we demonstrated that JNK-IN-5A and its derivatives markedly reduced intracellular triacylglycerol (TAG) accumulation during DNL induction. These compounds also significantly inhibited the expression of key DNL pathway proteins, including PKL, FASN, ACACA, SCD1, SREBP1-c, and ChREBP. Global transcriptomic analyses revealed that SET-151, SET-152, and SET-162 exhibited superior anti-steatotic effects compared to SET-130 and JNK-IN-5A. These three derivatives uniquely downregulated genes involved in pyruvate metabolism, bile acid synthesis, fatty acid metabolism, and glycolysis pathways, effects not observed with JNK-IN-5A alone. Additionally, Compass analysis indicated that treatment with SET-151, SET-152, and SET-162 led to significant alterations in metabolic reactions related to lipid metabolism, whereas JNK-IN-5A showed minimal impact. Finally, we evaluated JNK-IN-5A and SET-152 in a high-sucrose, high-fat diet-induced in vivo rat model of MASLD. Both compounds significantly reduced hepatic lipid accumulation, liver stiffness, and key biochemical markers of MASLD. Collectively, our findings identified SET-152 as a promising drug candidate for the treatment of MASLD. Biological sciences/Chemical biology/Metabolic pathways Biological sciences/Drug discovery/Biomarkers/Predictive markers Biological sciences/Computational biology and bioinformatics/Cellular signalling networks Biological sciences/Systems biology Pyruvate Kinase Liver and Red blood Cells (PKLR) c-Jun N-terminal kinase (JNK) family MASLD Small molecules Systems Biology In vitro & In vivo MASLD rat model Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 HIGHLIGHTS JNK-IN-5A and its novel derivatives (SET-151, SET-152, SET-162, SET-130) effectively inhibit PKL and key proteins involved in the DNL pathway. Novel small molecules significantly reduce DNL and triacylglycerol accumulation in an in vitro steatosis model. SET-151, SET-152, and SET-162 demonstrate superior anti-steatotic effects through transcriptomics and metabolic pathway analyses compared to JNK-IN-5A. SET-152 significantly improves hepatic steatosis, stiffness, and MASLD-related biomarkers in an in vivo rat model. DNL pathway inhibition emerges as a promising therapeutic strategy for MASLD. INTRODUCTION Metabolic dysfunction-associated steatotic liver disease (MASLD) is a chronic metabolic liver disease representing a major global health concern, driven by underlying metabolic dysfunction and strongly linked to dysregulated lipid metabolism, obesity and type 2 diabetes 1 , 2 . Macro vesicular steatosis causes chronic inflammation, which can develop into metabolic dysfunction-associated steatohepatitis (MASH) 3 . Chronic hepatic inflammation leads to collagen deposition and fibrotic tissue formation, ultimately progressing to cirrhosis 4 , 5 . Approximately 20% of MASH patients progress to cirrhosis 6 . The estimated global prevalence of MASLD among adults is approximately 32%, with a higher prevalence observed in males (40%) compared to females (26%) 7 . Currently, resmetirom is the only FDA-approved therapeutic agent for MASH 8 . Despite this advancement, there remains an urgent need for the development of additional effective therapies for MASH, and extensive research efforts are ongoing in this field. The liver, as a central regulator of systemic metabolism, takes up and processes various metabolites circulating in the blood. Free fatty acids (FFAs) entering the liver from the bloodstream are converted into triacylglycerols (TAGs) 9 . Excess carbohydrates are used in the synthesis of fatty acids through de novo lipogenesis (DNL) 10 . High-carbohydrate intake, particularly under conditions of hyperinsulinemia and hyperglycaemia, activates key transcription factors such as SREBP-1c and ChREBP 11 . These transcription factors upregulate the expression of critical DNL enzymes, leading to increased fatty acid and TG synthesis from glucose 12 . To develop effective therapeutic interventions for steatosis, current research is focused on targeting various key regulators within these pathways. Kristine G. et al. 13 reported the development of TLC-2716, an inverse agonist of the liver X receptor (LXR), which is currently undergoing Phase 1 clinical trials. LXR is a key transcription factor that forms a heterodimer with the retinoid X receptor (RXR) and plays a central role in promoting hepatic lipogenesis and steatosis 14 . By binding to LXR as an inverse agonist, TLC-2716 suppresses hepatic lipogenesis and fibrosis 13 , 15 . Another promising therapeutic approach targets hepatic thyroid hormone receptor β (THR-β). Resmetirom (MGL-3196), a selective THR-β agonist, has been developed to activate lipid catabolism in the liver and has recently received FDA approval following successful Phase 3 trials 8 , 16 , 17 . THR-β, activated by triiodothyronine (T3) thyroid hormone, regulates the expression of genes involved in systemic lipid reduction, increased bile acid synthesis, and enhanced fat oxidation 18 . Resmetirom has demonstrated therapeutic efficacy in reducing hepatic fat accumulation by selectively activating THR-β 18 – 21 . Lee et al. 22 applied a systems biology approach to compare healthy liver tissue with samples from individuals with MASLD and hepatocellular carcinoma (HCC). This study identified key genes that were highly upregulated in both MASLD and HCC, highlighting their potential as therapeutic targets. Among these, three genes, including pyruvate kinase liver (PKL) and red blood cell (PKLR), patatin-like phospholipase domain-containing 3 (PNPLA3), and proprotein convertase subtilisin/kexin type 9 (PCSK9), were identified as effective drug targets. PKLR, a key enzyme in the DNL pathway and lipid metabolism, plays a critical role in hepatic steatosis 23 , 24 . To advance the development of novel therapeutics for MASLD, we established an in vitro model of DNL-induced steatosis and performed computational drug repurposing to identify small-molecule regulators of PKLR. In our previous study, we established a HepG2 DNL steatosis model using insulin and the LXR agonist T0901317 25 . Treatment with insulin and T0901317 led to significant increases in intracellular TAG accumulation, upregulation of key DNL pathway transcription factors and enzymes, as well as enhanced glycolytic activity. To identify the modulators of PKLR, we applied a systems biology-driven drug repurposing approach and identified JNK-IN-5A as a small molecule that reduces PKL protein levels in HepG2 cells, exhibiting promising anti-steatotic effects in the HepG2 DNL steatosis model 25 , 26 . JNK-IN-5A is a potent and selective ATP-competitive inhibitor of the c-Jun N-terminal kinase (JNK) family. It has been reported that there are strong mechanistic links between the JNK signalling pathways and DNL. It has been widely used to modulate JNK pathways in the in vitro and in vivo models of metabolic disorders, certain types of cancer and neurodegeneration. In this study, we evaluated the effect of the JNK-IN-5A, a PKLR-suppressing small molecule that suppresses PKLR, using a HepG2 DNL steatosis model. We observed that JNK-IN-5A treatment significantly reduced TAG accumulation by downregulating DNL pathway protein expression in the HepG2 DNL steatosis model. Furthermore, we identified four novel derivatives of JNK-IN-5A, including SET-151, SET-152, SET-162 and SET-130 that exhibited potent inhibitory effects on DNL-induced steatosis in vitro . These derivatives effectively decreased TAG accumulation and suppressed DNL pathway activation. For comparative purposes, we also evaluated the impact of the THR-β agonist resmetirom 27 in the HepG2 DNL steatosis model. Additionally, RNA sequencing was performed to investigate the transcriptomic differences between the treatment groups. To further elucidate the mechanisms of action of JNK-IN-5A and its derivatives, we applied systems biology analyses to identify altered pathways and metabolic reactions. These analyses provided insights into how the compounds modulate key metabolic processes relevant to MASLD. Furthermore, we tested the tolerability of SET-152 in GLP-like toxicity study in rats and evaluated the therapeutic potential of SET-152 in an in vivo MASLD model induced by a high-sucrose and high-fat diet. Treatment with the compounds significantly improved MASLD-related symptoms, as demonstrated by magnetic resonance imaging (MRI), two-dimensional shear wave elastography (2D-SWE), liver histology, and blood biochemical markers. RESULTS JNK-IN-5A derivatives effectively inhibit the DNL pathway in a HepG2 steatosis model A systems biology approach previously identified PKL as one of the genes strongly associated with MASLD and HCC 22 . To explore the functional role of PKL in hepatic steatosis, we examined the expression of key genes involved in the DNL pathway following siRNA-mediated PKL knockdown in the HepG2 in vitro steatosis model (Figs. 1 A & 1 B). PKL knockdown significantly reduced the expression of PKL (9.5%), ChREBP (14.4%), FASN (63%), and ACACA (51.6%), while pyruvate kinase M2 (PKM2) expression remained unchanged. Building on these findings, we identified JNK-IN-5A as a small molecule that reduces PKL protein levels in HepG2 cells 25 , 26 . To enhance therapeutic efficacy, we synthesized a series of JNK-IN-5A derivatives and identified four compounds (SET-151, SET-152, SET-162, and SET-130) with more potent anti-steatotic activity 28 . We evaluated these compounds in the one-week HepG2 DNL steatosis model (Fig. 1 B). Treatment with 10 µM of each compound significantly reduced TAG accumulation (32.9% for JNK-IN-5A, 11.5% for SET-151, 10.7% for SET-152, 12.3% for SET-162, and 16.7% for SET-130). After normalization to cell viability, TAG reductions were 58.4% for JNK-IN-5A, 18.6% for SET-151, 24.5% for SET-152, 25.1% for SET-162, and 26.1% for SET-130 (Fig. 1 C). Western blot analysis revealed that all compounds significantly decreased the expression of key DNL pathway enzymes, including fatty acid synthase (FASN), acetyl-CoA carboxylase (ACACA), and stearoyl-CoA desaturase 1 (SCD-1), which catalyses monounsaturated fatty acid (MUFA) production and promotes hepatic TAG storage and VLDL secretion (Fig. 1 D). Additionally, the expression of DNL-regulating transcription factors, ChREBP and SREBP-1c, was markedly reduced in all treatment groups. Ito et al . 29 suggest that JNK2 plays a dominant role over JNK1 in regulating SREBP-1c activation. Consistent with this, JNK-IN-5A and its derivatives did not alter JNK1 protein expression, except for a modest reduction by SET-151 (0.65-fold). Interestingly, SET-151 (0.57-fold), SET-152 (0.66-fold), and SET-162 (0.57-fold) exhibited stronger inhibitory effects on JNK2 expression compared to SET-130 (0.81-fold) and JNK-IN-5A (0.75-fold). These mechanistic insights were further supported by Open mechanism of action (MoA) analysis 30 , which predicted two common mechanisms of action for all compounds: the 'JNK2–JUN–SREBP1-c' and 'JNK2–JUN–MXI1–ChREBP' signalling pathways (Supplementary Fig. 2). Notably, SET-151, SET-152, and SET-162 demonstrated lower penalty scores (1-confidence score) for the MXI1–ChREBP association than SET-130 and JNK-IN-5A, consistent with stronger downregulation of MLXIPL, the gene encoding ChREBP. Similarly, SREBF1, which encodes SREBP-1c, was significantly downregulated following treatment with all compounds, with high-confidence predictions for all interactions within the MoA network. Signal transducer and activator of transcription 1 (STAT1), a transcription factor implicated in the development of MASLD and MASH 31 , is part of the JNK3–STAT1–PKL regulatory axis 30 . Our data showed that while JNK-IN-5A and its derivatives robustly reduced JNK3 and PKL expression, only the derivatives substantially decreased STAT1 protein levels, indicating enhanced efficacy over JNK-IN-5A. Increased glycolysis is a characteristic metabolic change in liver steatosis 32 . Nuclear-localized PKM2, particularly in its monomeric or dimeric form, promotes glycolysis 33 . Phosphorylation at Tyr105 prevents PKM2 tetramer formation, enhancing its glycolytic activity 34 . While JNK-IN-5A and its derivatives did not affect total PKM2 protein levels in the HepG2 DNL steatosis model, Tyr105-phosphorylated PKM2 levels were dramatically reduced following treatment with all compounds. Based on these findings, we selected SET-151, SET-152, and SET-162 as the most potent derivatives, demonstrating superior therapeutic efficacy and distinct mechanisms compared to the reference compound JNK-IN-5A (Fig. 1 E). Their anti-steatotic effects were confirmed by BODIPY™ 493/503 staining for neutral lipids and Oil Red O staining for lipid droplets. In the BODIPY assay, SET-151 (52.1%), SET-152 (45.7%), and SET-162 (55.2%) significantly reduced neutral lipid accumulation compared to JNK-IN-5A (71.7%) (Fig. 1 E). Similarly, Oil Red O staining revealed greater reductions in intracellular lipid droplets with SET-151 (57.3%), SET-152 (51.9%), and SET-162 (46.9%) than with JNK-IN-5A (77.1%) (Fig. 1 F). Together, these results demonstrate that SET-151, SET-152 and SET-162 exhibit stronger anti-steatotic activity than JNK-IN-5A, primarily through the inhibition of the DNL pathway and the regulation of key metabolic and transcriptional pathways involved in MASLD progression. Comparison of Resmetirom and JNK-IN-5A derivatives in the HepG2 DNL steatosis model Resmetirom activates hepatic THR-β, promoting systemic lipid clearance by enhancing bile acid synthesis and fat oxidation 16 , 27 . In this study, we compared the therapeutic effects of resmetirom and JNK-IN-5A derivatives in the HepG2 DNL steatosis model (Fig. 2 ). Resmetirom treatment resulted in a dose-dependent reduction in intracellular TAG levels (Fig. 2 A). Specifically, 10µM (62.4%) and 5µM (78.2%) of resmetirom reduced the TAG content. SET-151 (42.8%), SET-152 (38.3%), and SET-162 (43.6%), also significantly reduced TAG content at 5 µM. When normalized to total protein content, 10µM (61.2%) and 5µM (75.7%) of resmetirom, 5µM (80.5%) of SET151, 5µM (59.9%) of SET152 and 5µM (61.8%) of SET162 reduced the TAG levels compared to the untreated control group. Western blot analysis of the DNL pathway proteins further demonstrated the superior inhibitory effects of JNK-IN-5A derivatives compared to resmetirom (Fig. 2 B). FASN (0.66 & 0.71), ACACA (0.84 & 0.80) and SCD1 (0.63 & 0.73) protein levels were decreased in the 5µM and 10µ resmetirom treated group. In contrast, treatment with 5 µM of SET-151, SET-152, and SET-162 resulted in more pronounced reductions: FASN (0.40, 0.38, 0.45), ACACA (0.22, 0.38, 0.79), and SCD1 (0.36, 0.42, 0.51), respectively. Moreover, the JNK-IN-5A derivatives showed more potent suppression of the key DNL transcription factors SREBP1-c and ChREBP compared to resmetirom. SREBP1-c expression was reduced by resmetirom at 5 µM (0.74) and 10 µM (0.87), whereas SET-151, SET-152, and SET-162 further decreased SREBP1-c expression (0.49, 0.68, and 0.86). ChREBP expression (0.29, 0.42, and 0.42) was also markedly reduced by JNK-IN-5A derivatives for SET-151, SET-152, and SET-162, compared to reductions observed with resmetirom at 5 µM (0.72) and 10 µM (0.75). Importantly, JNK-IN-5A derivatives almost completely suppressed PKL expression, reducing it to undetectable levels. In contrast, resmetirom-treated cells maintained 87% and 93% of PKL expression at 5 µM and 10 µM, respectively. PKM2 expression was moderately reduced across all treatment groups: resmetirom 5 µM (0.71), resmetirom 10 µM (0.85), SET-151 (0.86), SET-152 (0.80), and SET-162 (0.80). Lipid staining further confirmed these findings. BODIPY™ 493/503 staining demonstrated that 5 µM resmetirom reduced neutral lipid accumulation to 65.9% of the control level, whereas SET-151, SET-152, and SET-162 showed more pronounced reductions to 41.8%, 59.5%, and 43.6%, respectively (Fig. 2 C). Collectively, these results indicate that JNK-IN-5A derivatives exhibit more potent inhibitory effects on the DNL steatosis pathway and intracellular lipid accumulation compared to resmetirom, highlighting their potential as potent therapeutic candidates for MASLD. JNK-IN-5A derivatives exhibit distinct transcriptional profiles To investigate the transcriptional differences induced by JNK-IN-5A and its novel derivatives, we performed global RNA sequencing (RNA-seq) analysis on treated HepG2 cells. Principal Component Analysis (PCA) revealed three distinct gene expression clusters corresponding to the treatment groups (Fig. 3 A). SET-130 clustered closely with JNK-IN-5A, whereas SET-151, SET-152, and SET-162 formed a separate, tightly grouped cluster. All treatment groups were distinct from the untreated control group. In terms of global transcriptional impact, SET-151, SET-152, and SET-162 induced substantially higher numbers of differentially expressed genes (DEGs) compared to SET-130 and JNK-IN-5A (Fig. 3 B). Jaccard index analysis of the DEGs further confirmed that SET-151, SET-152, and SET-162 shared a high degree of similarity in both upregulated and downregulated gene sets (Fig. 3 C), suggesting a consistent and distinct mechanism of action among these three derivatives. To assess the functional consequences of these transcriptional changes, we first examined the expression of a previously defined MASLD-associated gene module (n = 40) 35 . All compounds, including JNK-IN-5A and its derivatives, significantly downregulated the expression of genes within this MASLD module (Fig. 3 D). Notably, SET-151, SET-152, and SET-162 produced a more pronounced downregulation effect, with a marked reduction in PKLR expression. Gene Set Overrepresentation Analysis (GSOA) revealed that the downregulated genes in all treatment groups were enriched in pathways related to lipid metabolism and glycolysis. Importantly, SET-151, SET-152, and SET-162 uniquely suppressed genes associated with fatty acid metabolism and primary bile acid biosynthesis, pathways highly relevant to MASLD pathogenesis (Fig. 3 E). To further explore the clinical relevance of these findings, we retrieved MASLD- and MASH-associated genes from the DisGeNET database (773 and 316 genes, respectively), 36 and performed Gene Set Enrichment Analysis (GSEA). The results demonstrated that all treatment groups reduced the enrichment scores of MASLD-associated genes; however, SET-151, SET-152, and SET-162 were significantly more effective in this regard (Fig. 3 F). Interestingly, a reduction in the enrichment scores of MASH-related genes was observed exclusively in cells treated with SET-151, SET-152, and SET-162, but not in cells treated with JNK-IN-5A or SET-130 (Fig. 3 F). Collectively, these transcriptomic analyses demonstrate that SET-151, SET-152, and SET-162 exhibit distinct and stronger transcriptional modulation compared to the parental compound JNK-IN-5A, supporting their superior therapeutic potential for MASLD and MASH. JNK-IN-5A derivatives exhibit distinct metabolic profiles To comprehensively characterize the metabolic alterations induced by JNK-IN-5A and its derivatives, we performed Compass metabolic activity analysis 37 based on global transcriptomic profiling. The analysis was conducted using the RECON3D human metabolic network model, which encompasses 10,600 reactions, 5,835 metabolites and 2,248 genes. PCA of Compass output revealed three distinct metabolic clusters corresponding to the treatment groups: (1) SET-151, SET-152, and SET-162; (2) SET-130 and JNK-IN-5A; and (3) the untreated control group (Fig. 4 A). These results highlight the distinct metabolic states induced by the three most potent JNK-IN-5A derivatives compared to both the parental compound and the control. We next performed differential reaction activity analysis to quantify metabolic alterations between the treatment and control groups. Cohen's d effect sizes were calculated for each reaction, and significance was determined using Benjamini-Hochberg adjusted Wilcoxon rank-sum tests (p < 0.05), excluding reactions classified under the "Miscellaneous" subsystem. The SET-151, SET-152, and SET-162 groups exhibited 9,214, 9,580, and 9,945 significantly altered reactions, respectively. In contrast, no significantly changed reactions were detected in the SET-130 and JNK-IN-5A groups. As shown in Fig. 4 B, reactions related to lipid metabolism, including glycolysis, cholesterol metabolism, fatty acid synthesis, and pyruvate metabolism, were significantly altered in response to SET-151, SET-152, and SET-162 treatment, compared to the control group. Notably, SET-130 and JNK-IN-5A treatments did not induce significant changes in these key metabolic pathways (Supplementary Fig. 1). To further assess disease-relevant metabolic changes, we extracted 115 MASLD-associated reactions from the Compass results. The majority of these reactions were involved in cholesterol metabolism, fatty acid synthesis, and glycolysis/gluconeogenesis pathways (Fig. 4 C). Remarkably, activity levels of the three pyruvate kinase isoforms encoded by the PKLR gene were significantly reduced following SET-151, SET-152, and SET-162 treatment. This finding suggests that these JNK-IN-5A derivatives effectively disrupt pyruvate kinase-regulated glycolysis, contributing to their therapeutic effects in MASLD. Collectively, these metabolic analyses demonstrate that SET-151, SET-152, and SET-162 induce distinct and robust metabolic remodelling compared to JNK-IN-5A, with pronounced inhibition of lipids and pyruvate metabolism, supporting their superior potential as therapeutic candidates for MASLD. GLP-like toxicity assessment of SET-152 in rats demonstrates its safety After testing the efficacy of SET-152 in an in vitro model of steatosis and revealing its MoA based on global transcriptomics and systems biology approach, we conducted a GLP-like 7-day oral tolerability study in Wistar rats to evaluate its safety at three dose levels (30, 100, and 300 mg/kg). The study included a vehicle control group (4 males and 4 females) and three dose groups, each consisting of 3 male and 3 female rats. The oral gavage dosing was administered once daily in the morning for seven consecutive days. No clinical adverse effects were observed in any of the treated animals after 7 days. Body weight, haematology, and plasma chemistry analysis were evaluated. Treatment had no significant effect on body weight (Fig. 5 A). While mean red cell haemoglobin concentration (MCHC) and blood urea nitrogen (BUN) levels showed statistically significant changes in the treated animals, these changes were randomly distributed across dose groups and exhibited no dose-dependent effects (Fig. 5 B). These changes were considered incidental and unrelated to the treatment by the toxicology experts at RISE, Sweden. All other parameters remained within normal ranges, further supporting the safety of the SET-152 (Supplementary Fig. 3). Evaluation of JNK-IN-5A and SET-152 in a high-fat, high-sucrose diet-induced in vivo MASH model To further assess the therapeutic potential of JNK-IN-5A and SET-152 in rats, we developed a high-fat, high-sucrose (HFHS) diet-induced MASH animal model (Fig. 6 A). Rats were fed the HFHS diet for 84 days (12 weeks) to induce MASH, which was confirmed using non-invasive methods, including two-dimensional (2D) shear wave elastography and magnetic resonance imaging (MRI). Following MASH induction, rats were treated with either JNK-IN-5A or SET-152 at a dose level of 30 mg/kg via oral gavage for 42 days (6 weeks). Body weight was monitored weekly throughout the study (Fig. 6 B). By 126 days, the MASH group exhibited a significant increase in body weight (343 ± 50 g) compared to the control group (300 ± 25.6 g). Notably, the JNK-IN-5A (304 ± 23.9 g) and SET-152 (298 ± 24.3 g) treatment groups maintained their body weights comparable to those of healthy controls. Liver stiffness, a marker of fibrosis, was evaluated using 2D shear wave elastography on day 126 (Fig. 6 C). The MASH group exhibited significantly increased liver stiffness (36.7 ± 3.5 kPa) compared to controls (25.2 ± 4.3 kPa). Treatment with JNK-IN-5A (29.2 ± 0.4 kPa) and SET-152 (29.1 ± 2.9 kPa) significantly reduced liver stiffness compared to the untreated MASH group. Hepatic fat content was assessed by MRI at day 84 (post-MASH induction) and day 126 (following treatment) (Fig. 6 D). 42 days of treatment with JNK-IN-5A (16.8 ± 10.3%) and SET-152 (15.0 ± 8.5%) significantly reduced hepatic fat accumulation compared to the untreated MASH group (24.5 ± 3.4%). Histopathological analyses were performed on liver tissues using haematoxylin and eosin (H&E) staining (Fig. 6 E). Steatosis, ballooning degeneration, and lobular inflammation were scored according to standard criteria: steatosis (0–3), ballooning (0–2), and inflammation (0–3). The MASH group displayed significant pathological features, with elevated steatosis (1.43 ± 0.98), ballooning degeneration (1.29 ± 0.49), and lobular inflammation (0.71 ± 0.49) scores. JNK-IN-5A treatment reduced steatosis (0.86 ± 0.90), ballooning degeneration (0.29 ± 0.76), and eliminated lobular inflammation. Remarkably, SET-152 treatment led to a 90% reduction in steatosis (0.14 ± 0.38), an 89% reduction in ballooning degeneration (0.14 ± 0.38), and an 80% reduction in lobular inflammation (0.14 ± 0.38). Blood biochemistry analyses were conducted at the end of the 126 days study to evaluate liver function and metabolic health (Table 1 ). The MASH group exhibited significant elevations in serum alanine aminotransferase (ALT) and TAG levels, indicative of liver injury and dyslipidaemia. Treatment with both JNK-IN-5A and SET-152 significantly reduced ALT and TAG levels, indicating an improvement in liver damage and metabolic dysfunction. To evaluate the safety of the compound in the MASH model, we performed a micronucleus (MN) assay to assess genotoxicity and conducted haematological analyses (Table 3). No significant differences in MN frequency were observed among the control, MASH, MASH + JNK-IN-5A, and MASH + SET-152 groups, indicating no detectable genotoxicity. Similarly, haematological parameters remained within normal ranges across all groups, suggesting that neither compound induced haematological toxicity. Collectively, these findings demonstrate that both JNK-IN-5A and its derivative SET-152 effectively attenuate MASH-related pathological features, including hepatic steatosis, fibrosis, inflammation, and biochemical markers, without inducing observable toxicity in an in vivo MASH model. Notably, SET-152 exhibited superior therapeutic efficacy, supporting its potential as a promising candidate for MASH treatment. Table 1 Blood biochemistry of liver injury and MASLD progression. Serum biochemical markers were measured to evaluate liver function and metabolic alterations associated with MASH induction and treatment. * p value < 0.05, **p value < 0.01, *** p value < 0.001, # p value < 0.05, ## p value < 0.01, ### p value < 0.001 to MASH group. Groups Control MASH MASH+ JNK-IN-5A MASH+ SET152 ALP (U/L) 158,71 ± 26,71 104,29 ± 27,35 ** 93,29 ± 27,67 *** 118,57 ± 21,97 AST (U/L) 236,11 ± 24,65 262,86 ± 41,5 225,39 ± 90,87 216,6 ± 40,84 ALT (U/L) 52,01 ± 17,5 118,63 ± 32,39 *** 32,68 ± 6,31 ### 38,37 ± 15,65 ### LDH (U/L) 1951,71 ± 485,58 2523,29 ± 515,64 1623,67 ± 541,00 2212,83 ± 389,82 Triglyceride (mg/dL) 81,71 ± 18,22 184,43 ± 53,89 *** 108,43 ± 31,66 ## 96,14 ± 37,73 ### Total Cholesterol (mg/dL) 61,57 ± 9,64 76,86 ± 4,91 78,86 ± 19,80 54,86 ± 8,30 # HDL Cholesterol (mg/dL) 41,00 ± 6,51 38,86 ± 6,31 42,14 ± 13,12 36,71 ± 8,18 LDL Cholesterol (mg/dL) 10,57 ± 2,51 12,86 ± 3,18 11,29 ± 2,21 8,43 ± 2,44 Total Protein (g/dL) 7,21 ± 0,50 7,56 ± 0,38 8,16 ± 0,99 * 7,54 ± 0,54 Albumin (g/dL) 3,20 ± 0,21 3,63 ± 0,22 3,81 ± 0,45 * 3,49 ± 0,30 Glucose (mg/dL) 145,57 ± 31,65 205,00 ± 88,24 208,43 ± 64,02 228,57 ± 165,46 Total Bilirubin (mg/dL) 0,09 ± 0,05 0,11 ± 0,07 0,10 ± 0,08 0,10 ± 0,05 Creatine (mg/dL) 0,44 ± 0,03 0,53 ± 0,14 0,53 ± 0,12 0,43 ± 0,16 Uric Acid (mg/dL) 0,98 ± 0,35 1,33 ± 0,24 1,61 ± 0,93 1,35 ± 0,45 Table 2 Blood haematology following MASH induction and treatment. Groups Control MASH MASH+ JNK-IN-5A MASH+ SET152 PCT (%) 0,68 ± 0,12 0,71 ± 0,10 0,77 ± 0,14 0,72 ± 0,16 PLT (10^3/µL) 845,57 ± 158,62 871,29 ± 128,53 947,71 ± 123,89 904,86 ± 140,16 MCHC (g/dL) 28,24 ± 0,96 28,97 ± 0,69 29,27 ± 1,14 29,71 ± 1,11 MCH (fL) 17,41 ± 0,60 17,84 ± 0,59 18,04 ± 0,48 17,81 ± 0,60 MCV (fL) 61,67 ± 2,07 61,51 ± 1,66 61,69 ± 2,87 60,11 ± 2,25 HCT (%) 54,40 ± 2,67 55,51 ± 2,31 55,41 ± 5,42 51,99 ± 3,93 HGB (g/dL) 15,19 ± 1,27 16,09 ± 0,76 16,20 ± 1,51 15,40 ± 1,27 RBC (10^6/µL) 8,83 ± 0,60 9,04 ± 0,52 8,98 ± 0,65 8,66 ± 0,73 WBC (10^3 /µL) 5,81 ± 2,27 4,70 ± 1,50 4,27 ± 1,67 5,75 ± 3,07 Haematological analysis was performed to evaluate systemic effects of MASH and subsequent treatment with JNK-IN-5A or SET152. Parameters include platelet count (PLT), procalcitonin (PCT), mean corpuscular haemoglobin concentration (MCHC), mean corpuscular haemoglobin (MCH), mean corpuscular volume (MCV), haematocrit (HCT), haemoglobin (HGB), red blood cell count (RBC), and white blood cell count (WBC). Table 3. Micronucleus assay. Femur bones marrow micronucleus ratio was calculated. DISCUSSION The dysregulation of hepatic lipid metabolism, particularly increased DNL, is a central feature of MASLD and its progressive form, MASH. Multiple enzymes in the DNL pathway have been identified as therapeutic targets, with several candidates showing promising results in clinical and preclinical studies. Denifanstat (TVB-2640), a FASN inhibitor currently in a Phase 2b clinical trial, suppresses FASN activity and reduces hepatic TAG accumulation by inhibiting DNL. In addition to its anti-steatotic effects, Denifanstat has been shown to attenuate steatohepatitis through the deactivation of hepatic stellate cells 38 – 40 and has demonstrated anti-cancer activity in models of lung carcinoma, breast cancer, astrocytoma, and colon cancer 41 – 44 . Similarly, Firsocostat, an ACACA inhibitor that has completed a Phase 2 clinical trial, effectively reduces hepatic DNL 45 and improves liver fibrosis 46 – 48 . Several preclinical ACACA inhibitors, including CP-640186 49,50 , Soraphen A 49 , 51 , and TOFA 52 , have also been reported to reduce weight gain, hepatic steatosis, and inflammation, while exhibiting anti-cancer properties. The anti-cancer effects of the FASN and ACACA-targeting molecules are attributed mainly to the suppression of lipid synthesis, a metabolic pathway essential for both energy storage and the production of key cellular components, including membranes and signalling molecules. Importantly, lipid biosynthesis is often markedly upregulated in cancer cells to support rapid proliferation, a phenomenon associated with the Warburg effect 53 . Our study expands on this therapeutic paradigm by identifying PKL isoform, encoded by the PKLR gene, as a novel, highly disease-associated target for MASLD and MASH. Systems biology analysis, combined with global transcriptomic profiling, has identified PKLR as one of the most significantly associated genes with MASLD and HCC 22 . PKLR catalyses the final step of glycolysis, linking glucose and pyruvate metabolism to the DNL pathway by providing precursors for fatty acid synthesis. Notably, the inhibition of PKLR is expected to suppress both glycolysis-derived substrate availability and downstream lipogenesis, offering a multifaceted approach to target metabolic dysregulation in MASLD. In this context, we applied a computational drug repurposing approach and identified JNK-IN-5A as a small molecule that modulates PKLR expression. Building on this, we synthesized and evaluated a series of JNK-IN-5A derivatives (SET-151, SET-152, SET-162, and SET-130), three of which (SET-151, SET-152, and SET-162) exhibited superior anti-steatotic efficacy. Our data demonstrate that these derivatives not only downregulate PKLR expression but also suppress the expression of critical enzymes in the DNL pathway (FASN, ACACA) and SCD. SCD is a well-established therapeutic target for MASLD and certain cancers 54 – 58 . Pharmacological inhibition of SCD has been shown to exert beneficial effects in metabolic diseases and cancer. For example, the SCD inhibitor E6446 suppresses both SCD expression and the transcription factor ATF3, leading to impaired adipogenic differentiation and reduced hepatic lipogenesis 59 . Furthermore, Ascenzi et al. demonstrated the pivotal role of SCD as a key therapeutic target in cancer 60 . Inhibition of SCD reduces MUFA synthesis, resulting in the accumulation of SFAs. Elevated intracellular SFA levels are known to trigger cellular stress responses, including enhanced autophagy. Excessive autophagy can, in turn, promote apoptosis, lipotoxicity, and ferroptosis. Based on these established mechanisms, we hypothesize that the marked reduction of SCD protein expression observed following treatment with JNK-IN-5A and its derivatives may contribute to the reduced cell viability seen in the HepG2 DNL steatosis model. The simultaneous downregulation of PKLR, FASN, ACACA, and SCD by our compounds offers a unique multiple-targeting strategy that disrupts both substrate supply and enzymatic execution of lipid biosynthesis. Further transcriptomic and metabolic modelling revealed that SET-151, SET-152, and SET-162 induced broad transcriptional changes beyond PKLR suppression, downregulating pathways essential for MASLD and MASH progression, including pyruvate metabolism, bile acid biosynthesis, fatty acid metabolism, and glycolysis. These transcriptomic effects were also revealed in Compass metabolic activity analysis, which demonstrated that only these three derivatives, but not JNK-IN-5A or SET-130, significantly altered reaction activities within key lipid metabolic pathways, including glycolysis and fatty acid synthesis, which are crucial biological pathways in MASLD and MASH progression 61 . Of particular interest, activity levels of all three pyruvate kinase isoforms were reduced, suggesting that the observed metabolic reprogramming is directly linked to PKLR downregulation and impaired glycolytic flux fuelling DNL. Mechanistically, the link between JNK signalling, SREBP-1 activation, and DNL regulation provides a plausible explanation for the broad inhibitory effects of JNK-IN-5A and its derivatives. Previous studies have shown that MAPK-mediated phosphorylation of SREBP-1 is critical for its activation and nuclear translocation, enabling the transcription of lipogenic genes such as FASN and ACACA 62 . Preventing phosphorylation by JNKs has been shown to protect against hepatic steatosis and visceral obesity in mice 63 . However, further studies are needed to elucidate the precise regulatory mechanisms underlying the DNL pathway inhibition mediated by JNK-IN-5A and its derivatives. Our data support this model, showing that SET-151, SET-152, and SET-162 more potently reduce SREBP-1 and ChREBP levels than JNK-IN-5A, accompanied by a more substantial reduction in downstream DNL enzymes. Importantly, in vivo studies using a HFHS diet-induced MASH rat model demonstrated that JNK-IN-5A and SET-152 reduced hepatic fat accumulation, liver stiffness, and key histological features of MASH, including steatosis, ballooning degeneration, and lobular inflammation. SET-152, in particular, showed more profound therapeutic effects than the reference compound, consistent with its superior in vitro activity. These beneficial effects were achieved without evidence of genotoxicity or haematological toxicity, underscoring the therapeutic potential of this new chemical class. Further mechanistic studies are warranted to fully elucidate the interplay between JNK signalling, PKLR expression, and SREBP-1 regulation, and to explore the translational potential of these compounds in human clinical settings. Collectively, our findings identify PKLR as a promising, previously underexplored therapeutic target for MASLD and MASH. The development of SET-152 is capable of simultaneously suppressing PKLR, FASN, ACACA, and SCD, representing a novel, multi-targeted approach to disrupt the metabolic underpinnings of hepatic steatosis and fibrosis. The superior efficacy of SET-152, as demonstrated through in vitro and in vivo analyses, highlights its potential as a first-in-class DNL and lipid metabolism modulating compound for MASLD and MASH therapy. METHODS Cell culture and DNL steatosis induction HepG2 wild-type cells (ATCC, ATCC HB-8065™) were purchased from the genome engineering company Synthego. Cells were maintained with RPMI 1640 (R2405, Sigma-Aldrich) supplemented with 10% fetal bovine serum (FBS, F7524, Sigma-Aldrich), 1% P/S (P4333, Sigma-Aldrich). 6x10 4 cells HepG2 cells were seeded into a 96-well plate format for assay, and 1x10 6 cells were plated into a 6-well plate for western blot and image analysis. DMEM high glucose (D0819, Sigma-Aldrich) with 10% FBS, 1% P/S supplemented with 10µg/ml insulin (I9278, Sigma-Aldrich), and 10µM T0901317 (T2320, Sigma-Aldrich) was changed to induce DNL steatosis in HepG2 cells. DNL steatosis media and compounds were changed for one week, with a 3-day, 2-day, and 2-day cycle. Triacylglycerol (TAG), Cell viability (MTT) assay, Oil Red O staining, and BODIPY™ staining TAG levels were quantified using the Triacylglycerol Assay Kit – Quantification (ab65336, Abcam). HepG2 cells were lysed and incubated with 50 µL of assay buffer containing 2 µL of Cholesterol Esterase/Lipase for 30 minutes at room temperature to hydrolyse TAGs. The cell lysate was then mixed with an additional 100 µL of assay buffer and centrifuged using a tabletop centrifuge. A 50 µL aliquot of the supernatant was combined with 50 µL of assay buffer containing 2 µL of Triglyceride Enzyme Mix and 2 µL of OxiRed Probe, followed by a 10-minute incubation at room temperature. Absorbance was measured at 570 nm using a microplate reader (Hidex Sense Beta Plus). Cell viability was assessed using the MTT (M6494, ThermoFisher) according to the manufacturer’s instructions. For Oil Red O staining, HepG2 cells were fixed with 4% formaldehyde for 30 min at room temperature. Neutral lipids were stained using the Oil Red O Staining Kit (MAK194, Sigma-Aldrich) following the manufacturer’s protocol. BODIPY™ 493/503 (D3922, Invitrogen) was used for fluorescent staining of intracellular TAGs. After fixation with 4% formaldehyde, cells were washed with PBS and incubated with 2 µM BODIPY™ 493/503 in PBS for 15 minutes in the dark. For counterstaining, 100 nM Phalloidin Alexa Fluor™ 594 (A12381, Invitrogen) in PBS was applied following PBS wash. Western blot analysis HepG2 cells were lysed with CelLytic M (C2978, Sigma-Aldrich) buffer. 20µg protein lysate was prepared with 2x Laemmli Sample Buffer (1610737, Biorad). SDS PAGE was conducted using Mini-PROTEAN® TGX™ Precast Gels (Bio-Rad) and transferred by Trans-Blot® Turbo™ Transfer System (Bio-Rad). FASN (ab22759, Abcam), ACACA (NBP2-55439, Novus), ChREBP (ab92809, Abcam), SREBP-1C (PA1 337, Invitrogen), SCD-1 (ab236868, abcam), STAT1 (HPA000982), JNK1 (ab199380, Abcam), JNK2 (ab76125, Abcam), JNK3 (MA5-35246, Invitrogen), PKL (06653, Sigma), PKM (4053S, Cell signalling), Tyr-105 p-PKM (3827S, Cell signalling), and GAPDH (ab8245, Abcam) were blotted as a primary antibody for overnight. Secondary antibodies, Goat Anti-Rabbit HRP (ab205718) and goat anti-mouse IgG-HRP (ab67895, Abcam) were blotted for one hour. The protein band was detected with ImageQuantTMLAS 500 (29-0050-63, GE). Library preparation and RNA-sequencing The Illumina Stranded Total RNA Prep, Ligation with Ribo-Zero Plus kit was used for the construction of NGS libraries. RNA samples were sequenced with 2x100 paired-end reads by the NovaSeq 6000 system. Raw sequencing data (.bcl) was converted to FastQ with DRAGEN Software (v3.9.5). The data was delivered in FASTQ format using Illumina 1.8 quality scores. RNA-seq data pre-processing The fastq files were first explored by FastQC (v0.11.9) for quality control. Gene expression count data was quantified using the standard protocol of Kallisto (v0.48.0). 64 We retrieve the reference cDNA (was GRCh38, Ensembl release 110 for Homo sapiens) for alignment and quantification from the Ensembl website. After filtering out non-protein-coding genes and genes with an average count of less than 5, the Kallisto data was used for the downstream analysis. There were 14,444 genes for the downstream analysis. Open Mechanism of Action (MoA) Open MoA 30 was used to predict the potential MoA for compounds. Open MoA integrated network was used as the reference network. Genes with TPM values more than 1.00 were used to build the HepG2 network. MAPK9 (JNK2) was set as the starting point and FDR values of DEGs were computed to construct the weighted network. Eventually, specific weighted subnetworks were built for each of the drugs. In terms of the MoA prediction, ‘most possible path’ function was used to identify the most potential MoA between JNK2-SREBP1-c and JNK2-ChREBP, respectively. Differential expression analysis DESeq2 R package (v1.36.0) 65 was used to identify the differentially expressed genes (DEGs). To better visualize the data, we adapted apeglm method for effect size shrinkage 66 . Adjusted P-value 1 for up-regulated genes and log 2 fold change < -1 for down-regulated genes. The Benjamini-Hochberg (BH) correction was used for multiple testing corrections. Jaccard index was used to assess the similarity among the DEGs across various treatment groups, which is defined as the size of the intersection divided by the size of the union of two gene sets. Principal component analysis Gene expression profiles after variance stabilizing transformation were used in Principal Component Analysis (PCA) to explore the sample distribution using the R package of pcaMethods (v1.92.0) 67 . Gene set functional analysis Gene set overrepresentation analysis (GSOA) was applied to determine whether known biological functions or processes were overrepresented in the DEGs induced by different treatments. Up and down-regulated genes of different treatment groups were extracted for genes of interest and all detected genes were extracted as the background genes. Then, GSOA was applied to determine whether a list of DEGs of interest was significantly associated with specific Gene Ontology (GO) biological process terms. We also performed gene set enrichment analysis (GSEA) 68 . For this, genes were sorted by log 2 fold change in descending order, and disease-related genes from DisGeNET 36 were tested for their significance. The R package clusterProfiler (v4.4.4) 69 , 70 was used for both GSOA and GSEA. Metabolic activity analysis We used Compass, a flux balance-based algorithm for metabolic model analysis 37 . Gene expression levels of all the samples were used as input. The model was created using Recon3D, which was downloaded from the BiGG Models platform 71 . GLP-like toxicity study in rats A 7-day oral toxicity study was conducted in Wistar rats (supplier: Envigo, Venray, Netherlands) to preliminarily assess the tolerability of SET-152 at dose levels of 30, 100, and 300 mg/kg body weight. The study included a vehicle control group (4 males and 4 females) and three SET-152-treated groups, each consisting of 3 male and 3 female rats. The vehicle consisted of 1.5% (w/w) hydroxypropyl methylcellulose (HPMC) and 1.5% (w/w) polysorbate 80 (PS80) in 10 mM phosphate-buffered saline (PBS, pH 7), administered at a dose volume of 5 mL/kg. Dosing was performed once daily in the morning for seven consecutive days. 0.5 mL of blood was collected into K₂EDTA tubes per animal for haematology analysis, and 0.6 mL of blood was collected into lithium heparin tubes for plasma chemistry analysis. All samples were analysed within 60 minutes of collection. The Exigo haematology analyser was calibrated against a reference sample provided by the manufacturer, and a complete control sample analysis cycle was performed before analysis. All animal procedures and ethical reviews were performed in accordance with the 2010/63/EU Directive on the protection of animals used for biomedical research. Animal management and MASH induction in vivo Sprague Dawley rats (age: 6–8 weeks; weight: 250 ± 17 g) were obtained from Experimental Research and Application Center of Atatürk University (ATADEM) with ethical approval. A total of four groups were established. The control group consisted of eight rats and continued to receive standard feed used under normal conditions throughout the study (Bayramoğlu Yem, Erzurum, TURKIYE). The remaining three groups, in which the MASH model was to be induced, were fed a specialized diet (MD.88137), the composition of high sucrose (34% by weight), high fat (21% by weight; 42% kcal from fat), cholesterol (0.2% total cholesterol). All groups had access to clean drinking water (Doyum Su, Erzurum, TURKIYE). The detailed composition of the diet is provided in the Supplementary Figure S3. All experimental groups were weighed weekly. 2D Shear Wave Elastography and Magnetic Resonance Imaging (MRI) Ultrasonography and 2D Shear Wave Elastography (2D-SWE) imaging were performed using the EPIQ Elite (Philips, Amsterdam, the Netherlands) device by two well-experienced radiologists. Five measurements with a 1 mm ROI diameter were taken from the liver with a high-frequency eL18-4 linear probe. The average stiffness values in kilopascals (kPa) of these five measurements were recorded. For the MRI measurement, animals were anesthetized intraperitoneally while lying supine with their hind limbs extended parallel to their body. Magnetic resonance imaging (MRI) was performed using a 3 Tesla clinical scanner (Magnetom Skyra; Siemens Healthineers, Erlangen, Germany), with the rats positioned in a prone position. T1 FL2D (Fast Low-Angle Shot Two-dimensional) sequences were used to obtain in-phase and out-of-phase images for evaluating and calculating fat fraction. The T1 FL2D sequence parameters included a TR (repetition time) of 110 ms, TE (echo time) of 1.40 ms, voxel size of 1.1×1.1 ×3.5 mm, 30 slices with a slice thickness of 3.5 mm. Manual measurements were conducted to calculate fat fraction. Signal intensities were detected with a 0.11 mm² ROI diameter from liver and with a 0.3–0.5 mm² ROI diameter from spleen, both on in-phase and out-of-phase images. Fat percentage was measured by 100 × (liver SIIP/spleen SIIP - liver SIOP/spleen SIOP) / (2 × liver SIIP/spleen SIIP) Histopathological analysis At the end of the experiment, tissue samples were fixed in 10% neutral buffered formaldehyde for 48 hours and processed using standard histological techniques. Following paraffin embedding, 4 µm-thick sections were prepared and stained with hematoxylin and eosin (H&E) for histopathological evaluation under a light microscope (Nikon Eclipse Ci). Histopathological assessment was performed based on characteristic morphological features. The severity of hepatic steatosis was graded separately, considering both histological activity and fibrosis stage. The activity level was assessed using the MAFLD Activity Score (NAS), calculated as the sum of three histological components—steatosis (0–3), lobular inflammation (0–3), and hepatocellular ballooning (0–2)—yielding a total score ranging from 0 to 8, as described by Kleiner et al 72 . Micronucleus assay in bone marrow smears Femur bones were isolated from sacrificed rats, and surrounding muscle tissue was removed. Bone marrow was flushed using a sterile syringe with 0.5 mL FBS and 0.5 mL DMEM into Falcon tubes. The suspension was centrifuged at 2000 rpm for 5 min, and the supernatant was discarded. The pellet was resuspended in a drop of FBS, homogenized, and smeared onto slides. After air-drying, slides were fixed in absolute methanol for 10 min. Staining was performed using a commercial kit (ChemBio Laboratory Research, Turkiye) following the manufacturer’s instructions. Slides were examined under a light microscope (100× oil immersion), and 1000 polychromatic erythrocytes (PCEs) per slide were scored to determine the frequency of micronuclei (MN). Declarations AUTHOR CONTRIBUTIONS Conceptualization, W.K. and A.M.; Methodology, W.K., M.L., X.L., S.Ö., E.Y., M.S., C.B., F.C.C., S.A.A., A.T.A., F.A., H.J., H.Y., S.I., J.S., S.A., B.B., J.B., M.U., C.Z., H.T.; Writing – Original Draft, W.K., M.L., X.L.; Writing – Review & Editing, W.K., M.L., A.M.; Supervision, A.M., C.Z., M.U., and H.T. ACKNOWLEDGMENTS This work was financially supported by ScandiEdge Therapeutics and Knut and Alice Wallenberg Foundation. M.Z.L. is being sponsored in her doctoral study by the China Scholarship Council (Grant No.202208440189) References Mardinoglu A, Palsson B (2025) Ø. Genome-scale models in human metabologenomics. Nat. Rev. Genet. 26, 123–140 Paschos P, Paletas K (2009) Non alcoholic fatty liver disease and metabolic syndrome. Hippokratia 13:9–19 Sinton MC (2020) Macrovesicular Steatosis in Nonalcoholic Fatty Liver Disease Is a Consequence of Purine Nucleotide Cycle Driven Fumarate Accumulation . http://biorxiv.org/lookup/doi/ 10.1101/2020.06.23.166728 doi:10.1101/2020.06.23.166728 Dhamija E, Paul SB, Kedia S (2019) Non-alcoholic fatty liver disease associated with hepatocellular carcinoma: An increasing concern. Indian J Med Res 149:9–17 Tesfay M, Goldkamp WJ, Neuschwander-Tetri BA (2018) NASH: The Emerging Most Common Form of Chronic Liver Disease. Mo Med 115:225–229 Sheka AC et al (2020) Nonalcoholic Steatohepatitis: A Review. JAMA 323, 1175–1183 Riazi K et al (2022) The prevalence and incidence of NAFLD worldwide: a systematic review and meta-analysis. Lancet Gastroenterol Hepatol 7:851–861 FDA approves (2024) first MASH drug. Nat Biotechnol 42:540–540 Bradbury MW (2006) Lipid Metabolism and Liver Inflammation. I. Hepatic fatty acid uptake: possible role in steatosis. Am J Physiol -Gastrointest Liver Physiol 290:G194–G198 Browning JD, Horton JD (2004) Molecular mediators of hepatic steatosis and liver injury. J Clin Invest 114:147–152 Sanders FWB, Griffin JL (2016) De novo lipogenesis in the liver in health and disease: more than just a shunting yard for glucose. Biol Rev 91:452–468 Canbay A, Bechmann L, Gerken G (2007) Lipid Metabolism in the Liver. Z Für Gastroenterol 45:35–41 Griffett K, Burris TP (2023) Development of LXR inverse agonists to treat MAFLD, NASH, and other metabolic diseases. Front Med 10:1102469 Shen Q et al (2011) Liver X receptor-retinoid X receptor (LXR-RXR) heterodimer cistrome reveals coordination of LXR and AP1 signaling in keratinocytes. J Biol Chem 286:14554–14563 Griffett K et al (2015) The LXR inverse agonist SR9238 suppresses fibrosis in a model of non-alcoholic steatohepatitis. Mol Metab 4:353–357 Harrison SA et al (2023) Resmetirom for nonalcoholic fatty liver disease: a randomized, double-blind, placebo-controlled phase 3 trial. Nat Med 29:2919–2928 Kelly MJ et al (2014) Discovery of 2-[3,5-dichloro-4-(5-isopropyl-6-oxo-1,6-dihydropyridazin-3-yloxy)phenyl]-3,5-dioxo-2,3,4,5-tetrahydro[1,2,4]triazine-6-carbonitrile (MGL-3196), a Highly Selective Thyroid Hormone Receptor β agonist in clinical trials for the treatment of dyslipidemia. J Med Chem 57:3912–3923 Kannt A et al (2021) Activation of thyroid hormone receptor-β improved disease activity and metabolism independent of body weight in a mouse model of non-alcoholic steatohepatitis and fibrosis. Br J Pharmacol 178:2412–2423 Hatziagelaki E, Paschou SA, Schön M, Psaltopoulou T, Roden M (2022) NAFLD and thyroid function: pathophysiological and therapeutic considerations. Trends Endocrinol Metab 33:755–768 Zhou J et al (2022) Thyroid Hormone Decreases Hepatic Steatosis, Inflammation, and Fibrosis in a Dietary Mouse Model of Nonalcoholic Steatohepatitis. Thyroid Off J Am Thyroid Assoc 32:725–738 Karim G, Department of Medicine MS, Israel N, York NY, Bansal USA (2023) Division of Liver Diseases, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA. Resmetirom: An Orally Administered, Small-molecule, Liver-directed, β-selective THR Agonist for the Treatment of Non-alcoholic Fatty Liver Disease and Non-alcoholic Steatohepatitis. Eur Endocrinol 19:60 Lee S et al (2017) Network analyses identify liver-specific targets for treating liver diseases. Mol Syst Biol 13:938 Liu Z et al (2019) Pyruvate kinase L/R is a regulator of lipid metabolism and mitochondrial function. Metab Eng 52:263–272 Yuan M et al (2025) Targeting PKLR in liver diseases. Trends Endocrinol Metab S1043276025000542. 10.1016/j.tem.2025.03.009 Kim W et al (2023) Characterization of an in vitro steatosis model simulating activated de novo lipogenesis in MAFLD patients. iScience 26:107727 Zhang C et al (2022) Discovery of therapeutic agents targeting PKLR for NAFLD using drug repositioning. eBioMedicine 83:104214 Harrison SA et al (2024) A Phase 3, Randomized, Controlled Trial of Resmetirom in NASH with Liver Fibrosis. N Engl J Med 390:497–509 Iqbal S et al (2024) Design and synthesis of novel JNK inhibitors targeting liver pyruvate kinase for the treatment of non-alcoholic fatty liver disease and hepatocellular carcinoma. Bioorg Chem 147:107425 Ito M et al (2013) A novel JNK2/SREBP-1c pathway involved in insulin-induced fatty acid synthesis in human adipocytes. J Lipid Res 54:1531–1540 Liao X et al (2023) Open MoA: Revealing the Mechanism of Action (MoA) based on Network Topology and Hierarchy. Bioinformatics btad666 10.1093/bioinformatics/btad666 Grohmann M et al (2018) Obesity Drives STAT-1-Dependent NASH and STAT-3-Dependent HCC. Cell 175:1289–1306e20 Lu Q et al (2021) Metabolic Changes of Hepatocytes in NAFLD. Front Physiol 12:710420 Yang W, Lu Z (2013) Nuclear PKM2 regulates the Warburg effect. Cell Cycle 12:3343–3347 Hitosugi T et al (2009) Tyrosine Phosphorylation Inhibits PKM2 to Promote the Warburg Effect and Tumor Growth. Sci Signal 2 Yang H et al (2021) A network-based approach reveals the dysregulated transcriptional regulation in non-alcoholic fatty liver disease. iScience 24:103222 Piñero J et al (2015) DisGeNET: a discovery platform for the dynamical exploration of human diseases and their genes. Database J. Biol. Databases Curation bav028 (2015) Wagner A et al (2021) Metabolic modeling of single Th17 cells reveals regulators of autoimmunity. Cell 184:4168–4185e21 Loomba R et al (2021) TVB-2640 (FASN Inhibitor) for the Treatment of Nonalcoholic Steatohepatitis: FASCINATE-1, a Randomized, Placebo-Controlled Phase 2a Trial. Gastroenterology 161:1475–1486 Syed-Abdul MM et al (2020) Fatty Acid Synthase Inhibitor TVB-2640 Reduces Hepatic de Novo Lipogenesis in Males With Metabolic Abnormalities. Hepatol Baltim Md 72:103–118 O’Farrell M et al (2022) FASN inhibition targets multiple drivers of NASH by reducing steatosis, inflammation and fibrosis in preclinical models. Sci Rep 12:15661 Fries BD, Tobias F, Wang Y, Holbrook JH, Hummon AB (2023) Lipidomics Profiling Reveals Differential Alterations after FAS Inhibition in 3D Colon Cancer Cell Culture Models. J Proteome Res acs jproteome. .3c00593 Kelly W et al (2023) Phase II Investigation of TVB-2640 (Denifanstat) with Bevacizumab in Patients with First Relapse High-Grade Astrocytoma. Clin Cancer Res Off J Am Assoc Cancer Res 29:2419–2425 Falchook G et al (2021) First-in-human study of the safety, pharmacokinetics, and pharmacodynamics of first-in-class fatty acid synthase inhibitor TVB-2640 alone and with a taxane in advanced tumors. EClinicalMedicine 34:100797 Cao D et al (2022) Discovery of a mammalian FASN inhibitor against xenografts of non-small cell lung cancer and melanoma. Signal Transduct Target Ther 7:273 Kirby BJ et al (2021) Organic Anion Transporting Polypeptide Inhibition Dramatically Increases Plasma Exposure but not Pharmacodynamic Effect nor Inferred Hepatic Intracellular Exposure of Firsocostat. Clin Pharmacol Ther 109:1334–1341 Loomba R et al (2021) Combination Therapies Including Cilofexor and Firsocostat for Bridging Fibrosis and Cirrhosis Attributable to NASH. Hepatol Baltim Md 73:625–643 Alkhouri N et al (2022) Safety and efficacy of combination therapy with semaglutide, cilofexor and firsocostat in patients with non-alcoholic steatohepatitis: A randomised, open-label phase II trial. J Hepatol 77:607–618 Alkhouri N, Lawitz E, Noureddin M, DeFronzo R, Shulman GI (2020) GS-0976 (Firsocostat): an investigational liver-directed acetyl-CoA carboxylase (ACC) inhibitor for the treatment of non-alcoholic steatohepatitis (NASH). Expert Opin Investig Drugs 29:135–141 Corominas-Faja B et al (2014) Chemical inhibition of acetyl-CoA carboxylase suppresses self-renewal growth of cancer stem cells. Oncotarget 5:8306–8316 Hess D, Chisholm JW, Igal RA (2010) Inhibition of stearoylCoA desaturase activity blocks cell cycle progression and induces programmed cell death in lung cancer cells. PLoS ONE 5:e11394 Schreurs M et al (2009) Soraphen, an inhibitor of the acetyl-CoA carboxylase system, improves peripheral insulin sensitivity in mice fed a high-fat diet. Diabetes Obes Metab 11:987–991 Li S et al (2013) TOFA suppresses ovarian cancer cell growth in vitro and in vivo. Mol Med Rep 8:373–378 Burns JS, Manda G (2017) Metabolic Pathways of the Warburg Effect in Health and Disease: Perspectives of Choice, Chain or Chance. Int J Mol Sci 18:2755 Sun Q et al (2024) SCD1 is the critical signaling hub to mediate metabolic diseases: Mechanism and the development of its inhibitors. Biomed Pharmacother 170:115586 Jeyakumar SM, Vajreswari A (2022) Stearoyl-CoA desaturase 1: A potential target for non-alcoholic fatty liver disease?-perspective on emerging experimental evidence. World J Hepatol 14:168–179 Min J-Y, Kim D-H (2023) Stearoyl-CoA Desaturase 1 as a Therapeutic Biomarker: Focusing on Cancer Stem Cells. Int J Mol Sci 24:8951 Sen U, Coleman C, Sen T (2023) Stearoyl coenzyme A desaturase-1: multitasker in cancer, metabolism, and ferroptosis. Trends Cancer 9:480–489 Katoh Y et al (2022) Inhibition of stearoyl-CoA desaturase 1 (SCD1) enhances the antitumor T cell response through regulating β-catenin signaling in cancer cells and ER stress in T cells and synergizes with anti-PD-1 antibody. J Immunother Cancer 10:e004616 Wang W et al (2023) Identification of novel SCD1 inhibitor alleviates nonalcoholic fatty liver disease: critical role of liver-adipose axis. Cell Commun Signal 21:268 Ascenzi F et al (2021) SCD1, autophagy and cancer: implications for therapy. J Exp Clin Cancer Res 40:265 Heeren J, Scheja L (2021) Metabolic-associated fatty liver disease and lipoprotein metabolism. Mol Metab 50:101238 Kotzka J et al (2011) Identification of a gene variant in the master regulator of lipid metabolism SREBP-1 in a family with a novel form of severe combined hypolipidemia. Atherosclerosis 218:134–143 Kotzka J et al (2012) Preventing phosphorylation of sterol regulatory element-binding protein 1a by MAP-kinases protects mice from fatty liver and visceral obesity. PLoS ONE 7:e32609 Bray NL, Pimentel H, Melsted P, Pachter L (2016) Near-optimal probabilistic RNA-seq quantification. Nat Biotechnol 34:525–527 Love MI, Huber W, Anders S (2014) Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 15:550 Zhu A, Ibrahim JG, Love MI (2019) Heavy-tailed prior distributions for sequence count data: removing the noise and preserving large differences. Bioinformatics 35:2084–2092 Stacklies W, Redestig H, Scholz M, Walther D, Selbig J (2007) pcaMethods–a bioconductor package providing PCA methods for incomplete data. Bioinforma Oxf Engl 23:1164–1167 Subramanian A et al (2005) Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl. Acad. Sci. 102, 15545–15550 Wu T et al (2021) clusterProfiler 4.0: A universal enrichment tool for interpreting omics data. Innov Camb Mass 2:100141 Yu G, Wang L-G, Han Y, He Q-Y (2012) clusterProfiler: an R package for comparing biological themes among gene clusters. Omics J Integr Biol 16:284–287 King ZA et al (2016) BiGG Models: A platform for integrating, standardizing and sharing genome-scale models. Nucleic Acids Res 44:D515–522 Kleiner DE et al (2005) Design and validation of a histological scoring system for nonalcoholic fatty liver disease. Hepatol Baltim Md 41:1313–1321 Additional Declarations There is NO Competing Interest. Supplementary Files SupplementaryFigures.docx Cite Share Download PDF Status: Under Review Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7114368","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":499928811,"identity":"19f6cb84-e90d-44b7-a450-7c00cd7fd106","order_by":0,"name":"Adil 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34774","correspondingAuthor":false,"prefix":"","firstName":"Sajda","middleName":"","lastName":"Ashraf","suffix":""},{"id":499928828,"identity":"070db06d-3979-44cd-8a48-d6f90bea2737","order_by":17,"name":"Burcu Belmen","email":"","orcid":"","institution":"Trustlife Labs Drug Research \u0026 Development Center Istanbul, TR 34774","correspondingAuthor":false,"prefix":"","firstName":"Burcu","middleName":"","lastName":"Belmen","suffix":""},{"id":499928829,"identity":"d49bf80c-7750-4b22-8636-0aff9cd9267e","order_by":18,"name":"Jan Boren","email":"","orcid":"https://orcid.org/0000-0003-0786-8091","institution":"Univ. Gothenburg","correspondingAuthor":false,"prefix":"","firstName":"Jan","middleName":"","lastName":"Boren","suffix":""},{"id":499928830,"identity":"6bd3ccc0-1a72-4905-b4c8-2eab079cfeed","order_by":19,"name":"Mathias Uhlén","email":"","orcid":"https://orcid.org/0000-0002-4858-8056","institution":"Science for Life Laboratory, KTH-Royal Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"Mathias","middleName":"","lastName":"Uhlén","suffix":""},{"id":499928831,"identity":"cf6c3968-4190-4031-856f-cb852ef4230a","order_by":20,"name":"Cheng Zhang","email":"","orcid":"https://orcid.org/0000-0002-3721-8586","institution":"KTH","correspondingAuthor":false,"prefix":"","firstName":"Cheng","middleName":"","lastName":"Zhang","suffix":""},{"id":499928832,"identity":"eddda673-c9a5-4091-8f99-189c040886ac","order_by":21,"name":"Hasan Turkez","email":"","orcid":"","institution":"Atatürk University","correspondingAuthor":false,"prefix":"","firstName":"Hasan","middleName":"","lastName":"Turkez","suffix":""}],"badges":[],"createdAt":"2025-07-13 15:55:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7114368/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7114368/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":92207560,"identity":"fd871107-3fb7-4542-8ef6-5a80243ecb80","added_by":"auto","created_at":"2025-09-25 19:08:42","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2077339,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eJNK-IN-5A derivatives decrease TAG levels in MASLD cell\u003c/strong\u003e\u003cem\u003e\u003cstrong\u003e \u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003emodels\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) siRNA inhibition of PKL expression suppresses the DNL pathway.\u003c/p\u003e\n\u003cp\u003e(B) Scheme of HepG2 DNL steatosis model induced by compound treatment. HepG2 cells underwent 1 week of DNL steatosis induction and were treated with 10µM compounds.\u003c/p\u003e\n\u003cp\u003e(C) TAG contents assay, Cell viability assay, and TAG contents normalized by Cell viability assay. Accumulated TAG on 1-week DNL steatosis and 10µM compounds-treated cells were investigated. Data are represented as mean ± SD, *p \u0026lt; 0.05, Student’s t test.\u003c/p\u003e\n\u003cp\u003e(D) Western blot analysis for the DNL pathway involved transcription factors and enzymes. Band intensity is measured using ImageJ.\u003c/p\u003e\n\u003cp\u003e(E) Fluorescence staining of lipid droplets and the cytoskeleton. Green: neutral lipids, Red: Cytoskeleton (F-actin), Scale bar, 100 μm. ROI of GFP intensity is measured with Image J. Data are represented as mean ± SD, *p \u0026lt; 0.05, Student’s t test.\u003c/p\u003e\n\u003cp\u003e(F) \u0026nbsp;Oil Red O staining. DIC image showing the accumulated lipid droplets. Scale bar, 100μm. ROI of Oil Red O intensity is measured with Image J. Data are represented as mean ± SD, *p \u0026lt; 0.05, Student’s t test.\u003c/p\u003e","description":"","filename":"Figure1121.png","url":"https://assets-eu.researchsquare.com/files/rs-7114368/v1/89782432b73b9ac3b417d0f4.png"},{"id":92207559,"identity":"30938ad8-3efa-4f9f-9615-1c556230429a","added_by":"auto","created_at":"2025-09-25 19:08:42","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":932818,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eJNK-IN-5A derivatives demonstrated comparable therapeutic efficacy to resmetirom in MASLD cell\u003c/strong\u003e\u003cem\u003e\u003cstrong\u003e \u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003emodels\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) TAG contents assay, and TAG contents normalized by protein quantification. Accumulated TAG on 1-week DNL steatosis with resmetirom 10 µM, 5 µM, and 5 µM compound-treated cells was investigated. Data are represented as mean ± SD, *p \u0026lt; 0.05, Student’s t test.\u003c/p\u003e\n\u003cp\u003e(B) Comparison of resmetirom and JNK-IN-5A derivatives for the DNL pathway, involving transcription factor and enzyme protein expression levels via Western blot analysis. Band intensity is measured using ImageJ.\u003c/p\u003e\n\u003cp\u003e(C) 5µM resmetirom and JNK-IN-5A derivatives lipid droplet and cytoskeleton fluorescence staining. Green: neutral lipids, Red: Cytoskeleton (F-actin), Scale bar, 100 μm. ROI of GFP intensity is measured with Image J. Data are represented as mean ± SD, *p \u0026lt; 0.05, Student’s t test.\u003c/p\u003e","description":"","filename":"Figure1122.png","url":"https://assets-eu.researchsquare.com/files/rs-7114368/v1/b0ecd54bc799719ed137c24c.png"},{"id":92207533,"identity":"751cfa6a-d871-466c-874f-c6e2575487de","added_by":"auto","created_at":"2025-09-25 19:08:41","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":886360,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eJNK-IN-5A derivatives exhibited distinct transcriptional profiles compared to the reference compound.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) PCA plot showing sample distribution of SET151, SET152, SET162, SET130, JNK-IN-5A, and Control groups (both n = 4) based on principal component analysis of RNA-seq gene expression profile.\u003c/p\u003e\n\u003cp\u003e(B) Bar plot showing the numbers of differentially expressed genes (DEGs) in each treatment group\u003c/p\u003e\n\u003cp\u003e(C) Heatmaps of the Jaccard index showing the similarity of up- and down-regulated genes across different treatment groups\u003c/p\u003e\n\u003cp\u003e(D) Heatmap showing the differential expression (presented as log2 fold change) of 40 genes derived from the MASLD-associated module in different treatment groups. ∗ Represent significantly differentially expressed genes (DEGs) in each group (cut-off p values for DEGs (p \u0026lt; 0.05)\u003c/p\u003e\n\u003cp\u003e(E) Dot plot showing the GSOA results conducted based on the biological process category from the Gene Ontology (GO) dataset. The size of the dot indicates the gene ratio, i.e., the DEGs assigned to the corresponding pathway relative to the total analysed DEGs., and the dot's colour indicates the adjusted p value.\u003c/p\u003e\n\u003cp\u003e(F) Bar plot showing the normalized enrichment score (NES) of metabolic dysfunction–associated steatotic liver disease (MASLD) and metabolic dysfunction–associated steatohepatitis (MASH) related genes.\u003c/p\u003e","description":"","filename":"Figure1123.png","url":"https://assets-eu.researchsquare.com/files/rs-7114368/v1/72ada426844d7b09a66d7993.png"},{"id":92207556,"identity":"bbe179fe-07d7-441b-8a00-822b7d3873a9","added_by":"auto","created_at":"2025-09-25 19:08:42","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":652864,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eJNK-IN-5A derivatives exhibited distinct metabolic profiles compared to the reference compound\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) PCA plot showing the distinct metabolic profiles among SET151, SET152, and SET172 group, SET130, JNK-IN-5A group and Control group (both n = 4)\u003c/p\u003e\n\u003cp\u003e(B) Compass-score differential activity test between SET151 and the Control group, SET152 and the Control group, as well as SET162 and the Control group, respectively.\u003c/p\u003e\n\u003cp\u003e(C) Heatmap illustrating the differential activity of MASLD genes associated with subsystems across various treatment groups.\u003c/p\u003e","description":"","filename":"Figure1124.png","url":"https://assets-eu.researchsquare.com/files/rs-7114368/v1/709748028c12335d032be23a.png"},{"id":92207506,"identity":"70a8d743-514c-4973-bbd0-ca23d680047f","added_by":"auto","created_at":"2025-09-25 19:08:40","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":477603,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGLP-like toxicity study supporting the safety of the compound in rats.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Body weight changes over time across treatment groups.\u003c/p\u003e\n\u003cp\u003e(B) Mean corpuscular haemoglobin concentration (MCHC) and blood urea nitrogen (BUN) levels showed statistically significant changes over time, but without a clear dose-dependent pattern.\u003c/p\u003e","description":"","filename":"Figure1125.png","url":"https://assets-eu.researchsquare.com/files/rs-7114368/v1/386224efcf04a0a96a348752.png"},{"id":92207588,"identity":"245543f4-1ec6-4a02-81f8-d35646586cea","added_by":"auto","created_at":"2025-09-25 19:08:43","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1074755,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSET152 exhibited distinct therapeutic efficacy in reversing MASH in vivo.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Schematic overview of \u003cem\u003ein vivo\u003c/em\u003e study. 6-8 weeks SD rats underwent MASH induction via a high-sucrose and fat diet for 84 days. JNK-5A-IN and SET152 were treated for the following 42 days.\u003c/p\u003e\n\u003cp\u003e(B) Body weight was measured weekly over the 126 days experimental period.\u003c/p\u003e\n\u003cp\u003e(C) Liver stiffness (kPa) was recorded at baseline (Day 0), after MASH induction (Day 84), and at the end of drug treatment (Day 126).\u003c/p\u003e\n\u003cp\u003e(D) MRI measured liver fat content.\u003c/p\u003e\n\u003cp\u003e(E) Haematoxylin and eosin (H\u0026amp;E) staining for liver tissue collected at day 126. Histological features are indicated as follows: portal area (blue arrow), hepatic cord (blue bracket), panlobular steatosis (black bracket), macrovesicular steatosis-macro droplet (black arrow), macrovesicular steatosis-micro droplet (red arrow), hepatocytes with ballooning degeneration (yellow arrow), lobular inflammation (green arrow), and central vein (gray arrow). Data are represented as mean ± SD, Student's t test, * Represent statistical significance compared to the control group (*p \u0026lt; 0.05, **p \u0026lt; 0.01, ***p \u0026lt; 0.001). # Represent statistical significance relative to the MASH group (#p \u0026lt; 0.05, ##p \u0026lt; 0.01, ###p \u0026lt; 0.001).\u003c/p\u003e","description":"","filename":"Figure1126.png","url":"https://assets-eu.researchsquare.com/files/rs-7114368/v1/7d2681004a206772c0e930a9.png"},{"id":92207921,"identity":"86a42937-40fa-4ada-b10a-225bafc62f7f","added_by":"auto","created_at":"2025-09-25 19:16:43","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":7359237,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7114368/v1/12618e37-4036-4575-a69f-bdf8df52f9af.pdf"},{"id":92207557,"identity":"e3b05aef-3c47-4486-8f16-95017ac624eb","added_by":"auto","created_at":"2025-09-25 19:08:42","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":360253,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigures.docx","url":"https://assets-eu.researchsquare.com/files/rs-7114368/v1/c6d0dcea09fca97ac53e06bc.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Targeting PKLR and lipogenic enzymes through JNK inhibition to develop a therapeutic strategy for MASLD and MASH","fulltext":[{"header":"HIGHLIGHTS","content":"\u003cul\u003e\n \u003cli\u003eJNK-IN-5A and its novel derivatives (SET-151, SET-152, SET-162, SET-130) effectively inhibit PKL and key proteins involved in the DNL pathway.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cul\u003e\n \u003cli\u003eNovel small molecules significantly reduce DNL and triacylglycerol accumulation in an \u003cem\u003ein vitro\u003c/em\u003e steatosis model.\u003c/li\u003e\n \u003cli\u003eSET-151, SET-152, and SET-162 demonstrate superior anti-steatotic effects through transcriptomics and metabolic pathway analyses compared to JNK-IN-5A.\u003c/li\u003e\n \u003cli\u003eSET-152 significantly improves hepatic steatosis, stiffness, and MASLD-related biomarkers in an \u003cem\u003ein vivo\u003c/em\u003e rat model.\u003c/li\u003e\n \u003cli\u003eDNL pathway inhibition emerges as a promising therapeutic strategy for MASLD.\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"INTRODUCTION","content":"\u003cp\u003eMetabolic dysfunction-associated steatotic liver disease (MASLD) is a chronic metabolic liver disease representing a major global health concern, driven by underlying metabolic dysfunction and strongly linked to dysregulated lipid metabolism, obesity and type 2 diabetes \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Macro vesicular steatosis causes chronic inflammation, which can develop into metabolic dysfunction-associated steatohepatitis (MASH) \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Chronic hepatic inflammation leads to collagen deposition and fibrotic tissue formation, ultimately progressing to cirrhosis \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Approximately 20% of MASH patients progress to cirrhosis \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. The estimated global prevalence of MASLD among adults is approximately 32%, with a higher prevalence observed in males (40%) compared to females (26%) \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Currently, resmetirom is the only FDA-approved therapeutic agent for MASH \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Despite this advancement, there remains an urgent need for the development of additional effective therapies for MASH, and extensive research efforts are ongoing in this field.\u003c/p\u003e\u003cp\u003eThe liver, as a central regulator of systemic metabolism, takes up and processes various metabolites circulating in the blood. Free fatty acids (FFAs) entering the liver from the bloodstream are converted into triacylglycerols (TAGs) \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Excess carbohydrates are used in the synthesis of fatty acids through \u003cem\u003ede novo\u003c/em\u003e lipogenesis (DNL) \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. High-carbohydrate intake, particularly under conditions of hyperinsulinemia and hyperglycaemia, activates key transcription factors such as SREBP-1c and ChREBP \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. These transcription factors upregulate the expression of critical DNL enzymes, leading to increased fatty acid and TG synthesis from glucose \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. To develop effective therapeutic interventions for steatosis, current research is focused on targeting various key regulators within these pathways.\u003c/p\u003e\u003cp\u003eKristine G. et al. \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e reported the development of TLC-2716, an inverse agonist of the liver X receptor (LXR), which is currently undergoing Phase 1 clinical trials. LXR is a key transcription factor that forms a heterodimer with the retinoid X receptor (RXR) and plays a central role in promoting hepatic lipogenesis and steatosis \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. By binding to LXR as an inverse agonist, TLC-2716 suppresses hepatic lipogenesis and fibrosis \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Another promising therapeutic approach targets hepatic thyroid hormone receptor β (THR-β). Resmetirom (MGL-3196), a selective THR-β agonist, has been developed to activate lipid catabolism in the liver and has recently received FDA approval following successful Phase 3 trials \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. THR-β, activated by triiodothyronine (T3) thyroid hormone, regulates the expression of genes involved in systemic lipid reduction, increased bile acid synthesis, and enhanced fat oxidation \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Resmetirom has demonstrated therapeutic efficacy in reducing hepatic fat accumulation by selectively activating THR-β \u003csup\u003e\u003cspan additionalcitationids=\"CR19 CR20\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e–\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eLee et al. \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e applied a systems biology approach to compare healthy liver tissue with samples from individuals with MASLD and hepatocellular carcinoma (HCC). This study identified key genes that were highly upregulated in both MASLD and HCC, highlighting their potential as therapeutic targets. Among these, three genes, including pyruvate kinase liver (PKL) and red blood cell (PKLR), patatin-like phospholipase domain-containing 3 (PNPLA3), and proprotein convertase subtilisin/kexin type 9 (PCSK9), were identified as effective drug targets. PKLR, a key enzyme in the DNL pathway and lipid metabolism, plays a critical role in hepatic steatosis \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. To advance the development of novel therapeutics for MASLD, we established an \u003cem\u003ein vitro\u003c/em\u003e model of DNL-induced steatosis and performed computational drug repurposing to identify small-molecule regulators of PKLR. In our previous study, we established a HepG2 DNL steatosis model using insulin and the LXR agonist T0901317 \u003csup\u003e25\u003c/sup\u003e. Treatment with insulin and T0901317 led to significant increases in intracellular TAG accumulation, upregulation of key DNL pathway transcription factors and enzymes, as well as enhanced glycolytic activity.\u003c/p\u003e\u003cp\u003eTo identify the modulators of PKLR, we applied a systems biology-driven drug repurposing approach and identified JNK-IN-5A as a small molecule that reduces PKL protein levels in HepG2 cells, exhibiting promising anti-steatotic effects in the HepG2 DNL steatosis model \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. JNK-IN-5A is a potent and selective ATP-competitive inhibitor of the c-Jun N-terminal kinase (JNK) family. It has been reported that there are strong mechanistic links between the JNK signalling pathways and DNL. It has been widely used to modulate JNK pathways in the \u003cem\u003ein vitro\u003c/em\u003e and \u003cem\u003ein vivo\u003c/em\u003e models of metabolic disorders, certain types of cancer and neurodegeneration.\u003c/p\u003e\u003cp\u003eIn this study, we evaluated the effect of the JNK-IN-5A, a PKLR-suppressing small molecule that suppresses PKLR, using a HepG2 DNL steatosis model. We observed that JNK-IN-5A treatment significantly reduced TAG accumulation by downregulating DNL pathway protein expression in the HepG2 DNL steatosis model. Furthermore, we identified four novel derivatives of JNK-IN-5A, including SET-151, SET-152, SET-162 and SET-130 that exhibited potent inhibitory effects on DNL-induced steatosis \u003cem\u003ein vitro\u003c/em\u003e. These derivatives effectively decreased TAG accumulation and suppressed DNL pathway activation. For comparative purposes, we also evaluated the impact of the THR-β agonist resmetirom \u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e in the HepG2 DNL steatosis model. Additionally, RNA sequencing was performed to investigate the transcriptomic differences between the treatment groups. To further elucidate the mechanisms of action of JNK-IN-5A and its derivatives, we applied systems biology analyses to identify altered pathways and metabolic reactions. These analyses provided insights into how the compounds modulate key metabolic processes relevant to MASLD. Furthermore, we tested the tolerability of SET-152 in GLP-like toxicity study in rats and evaluated the therapeutic potential of SET-152 in an \u003cem\u003ein vivo\u003c/em\u003e MASLD model induced by a high-sucrose and high-fat diet. Treatment with the compounds significantly improved MASLD-related symptoms, as demonstrated by magnetic resonance imaging (MRI), two-dimensional shear wave elastography (2D-SWE), liver histology, and blood biochemical markers.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003e\u003cb\u003eJNK-IN-5A derivatives effectively inhibit the DNL pathway in a HepG2 steatosis model\u003c/b\u003e\u003c/p\u003e\u003cp\u003eA systems biology approach previously identified PKL as one of the genes strongly associated with MASLD and HCC \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. To explore the functional role of PKL in hepatic steatosis, we examined the expression of key genes involved in the DNL pathway following siRNA-mediated PKL knockdown in the HepG2 \u003cem\u003ein vitro\u003c/em\u003e steatosis model (Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA \u0026amp; \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). PKL knockdown significantly reduced the expression of PKL (9.5%), ChREBP (14.4%), FASN (63%), and ACACA (51.6%), while pyruvate kinase M2 (PKM2) expression remained unchanged.\u003c/p\u003e\u003cp\u003eBuilding on these findings, we identified JNK-IN-5A as a small molecule that reduces PKL protein levels in HepG2 cells \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. To enhance therapeutic efficacy, we synthesized a series of JNK-IN-5A derivatives and identified four compounds (SET-151, SET-152, SET-162, and SET-130) with more potent anti-steatotic activity \u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. We evaluated these compounds in the one-week HepG2 DNL steatosis model (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). Treatment with 10 \u0026micro;M of each compound significantly reduced TAG accumulation (32.9% for JNK-IN-5A, 11.5% for SET-151, 10.7% for SET-152, 12.3% for SET-162, and 16.7% for SET-130). After normalization to cell viability, TAG reductions were 58.4% for JNK-IN-5A, 18.6% for SET-151, 24.5% for SET-152, 25.1% for SET-162, and 26.1% for SET-130 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). Western blot analysis revealed that all compounds significantly decreased the expression of key DNL pathway enzymes, including fatty acid synthase (FASN), acetyl-CoA carboxylase (ACACA), and stearoyl-CoA desaturase 1 (SCD-1), which catalyses monounsaturated fatty acid (MUFA) production and promotes hepatic TAG storage and VLDL secretion (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD). Additionally, the expression of DNL-regulating transcription factors, ChREBP and SREBP-1c, was markedly reduced in all treatment groups.\u003c/p\u003e\u003cp\u003eIto \u003cem\u003eet al\u003c/em\u003e. \u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e suggest that JNK2 plays a dominant role over JNK1 in regulating SREBP-1c activation. Consistent with this, JNK-IN-5A and its derivatives did not alter JNK1 protein expression, except for a modest reduction by SET-151 (0.65-fold). Interestingly, SET-151 (0.57-fold), SET-152 (0.66-fold), and SET-162 (0.57-fold) exhibited stronger inhibitory effects on JNK2 expression compared to SET-130 (0.81-fold) and JNK-IN-5A (0.75-fold). These mechanistic insights were further supported by Open mechanism of action (MoA) analysis \u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e, which predicted two common mechanisms of action for all compounds: the 'JNK2\u0026ndash;JUN\u0026ndash;SREBP1-c' and 'JNK2\u0026ndash;JUN\u0026ndash;MXI1\u0026ndash;ChREBP' signalling pathways (Supplementary Fig.\u0026nbsp;2). Notably, SET-151, SET-152, and SET-162 demonstrated lower penalty scores (1-confidence score) for the MXI1\u0026ndash;ChREBP association than SET-130 and JNK-IN-5A, consistent with stronger downregulation of MLXIPL, the gene encoding ChREBP. Similarly, SREBF1, which encodes SREBP-1c, was significantly downregulated following treatment with all compounds, with high-confidence predictions for all interactions within the MoA network. Signal transducer and activator of transcription 1 (STAT1), a transcription factor implicated in the development of MASLD and MASH \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e, is part of the JNK3\u0026ndash;STAT1\u0026ndash;PKL regulatory axis \u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. Our data showed that while JNK-IN-5A and its derivatives robustly reduced JNK3 and PKL expression, only the derivatives substantially decreased STAT1 protein levels, indicating enhanced efficacy over JNK-IN-5A.\u003c/p\u003e\u003cp\u003eIncreased glycolysis is a characteristic metabolic change in liver steatosis \u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Nuclear-localized PKM2, particularly in its monomeric or dimeric form, promotes glycolysis \u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. Phosphorylation at Tyr105 prevents PKM2 tetramer formation, enhancing its glycolytic activity \u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. While JNK-IN-5A and its derivatives did not affect total PKM2 protein levels in the HepG2 DNL steatosis model, Tyr105-phosphorylated PKM2 levels were dramatically reduced following treatment with all compounds.\u003c/p\u003e\u003cp\u003eBased on these findings, we selected SET-151, SET-152, and SET-162 as the most potent derivatives, demonstrating superior therapeutic efficacy and distinct mechanisms compared to the reference compound JNK-IN-5A (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE). Their anti-steatotic effects were confirmed by BODIPY\u0026trade; 493/503 staining for neutral lipids and Oil Red O staining for lipid droplets. In the BODIPY assay, SET-151 (52.1%), SET-152 (45.7%), and SET-162 (55.2%) significantly reduced neutral lipid accumulation compared to JNK-IN-5A (71.7%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE). Similarly, Oil Red O staining revealed greater reductions in intracellular lipid droplets with SET-151 (57.3%), SET-152 (51.9%), and SET-162 (46.9%) than with JNK-IN-5A (77.1%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eF). Together, these results demonstrate that SET-151, SET-152 and SET-162 exhibit stronger anti-steatotic activity than JNK-IN-5A, primarily through the inhibition of the DNL pathway and the regulation of key metabolic and transcriptional pathways involved in MASLD progression.\u003c/p\u003e\u003cp\u003e\u003cb\u003eComparison of Resmetirom and JNK-IN-5A derivatives in the HepG2 DNL steatosis model\u003c/b\u003e\u003c/p\u003e\u003cp\u003eResmetirom activates hepatic THR-β, promoting systemic lipid clearance by enhancing bile acid synthesis and fat oxidation \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. In this study, we compared the therapeutic effects of resmetirom and JNK-IN-5A derivatives in the HepG2 DNL steatosis model (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Resmetirom treatment resulted in a dose-dependent reduction in intracellular TAG levels (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Specifically, 10\u0026micro;M (62.4%) and 5\u0026micro;M (78.2%) of resmetirom reduced the TAG content. SET-151 (42.8%), SET-152 (38.3%), and SET-162 (43.6%), also significantly reduced TAG content at 5 \u0026micro;M. When normalized to total protein content, 10\u0026micro;M (61.2%) and 5\u0026micro;M (75.7%) of resmetirom, 5\u0026micro;M (80.5%) of SET151, 5\u0026micro;M (59.9%) of SET152 and 5\u0026micro;M (61.8%) of SET162 reduced the TAG levels compared to the untreated control group.\u003c/p\u003e\u003cp\u003eWestern blot analysis of the DNL pathway proteins further demonstrated the superior inhibitory effects of JNK-IN-5A derivatives compared to resmetirom (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). FASN (0.66 \u0026amp; 0.71), ACACA (0.84 \u0026amp; 0.80) and SCD1 (0.63 \u0026amp; 0.73) protein levels were decreased in the 5\u0026micro;M and 10\u0026micro; resmetirom treated group. In contrast, treatment with 5 \u0026micro;M of SET-151, SET-152, and SET-162 resulted in more pronounced reductions: FASN (0.40, 0.38, 0.45), ACACA (0.22, 0.38, 0.79), and SCD1 (0.36, 0.42, 0.51), respectively.\u003c/p\u003e\u003cp\u003eMoreover, the JNK-IN-5A derivatives showed more potent suppression of the key DNL transcription factors SREBP1-c and ChREBP compared to resmetirom. SREBP1-c expression was reduced by resmetirom at 5 \u0026micro;M (0.74) and 10 \u0026micro;M (0.87), whereas SET-151, SET-152, and SET-162 further decreased SREBP1-c expression (0.49, 0.68, and 0.86). ChREBP expression (0.29, 0.42, and 0.42) was also markedly reduced by JNK-IN-5A derivatives for SET-151, SET-152, and SET-162, compared to reductions observed with resmetirom at 5 \u0026micro;M (0.72) and 10 \u0026micro;M (0.75). Importantly, JNK-IN-5A derivatives almost completely suppressed PKL expression, reducing it to undetectable levels. In contrast, resmetirom-treated cells maintained 87% and 93% of PKL expression at 5 \u0026micro;M and 10 \u0026micro;M, respectively. PKM2 expression was moderately reduced across all treatment groups: resmetirom 5 \u0026micro;M (0.71), resmetirom 10 \u0026micro;M (0.85), SET-151 (0.86), SET-152 (0.80), and SET-162 (0.80).\u003c/p\u003e\u003cp\u003eLipid staining further confirmed these findings. BODIPY\u0026trade; 493/503 staining demonstrated that 5 \u0026micro;M resmetirom reduced neutral lipid accumulation to 65.9% of the control level, whereas SET-151, SET-152, and SET-162 showed more pronounced reductions to 41.8%, 59.5%, and 43.6%, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). Collectively, these results indicate that JNK-IN-5A derivatives exhibit more potent inhibitory effects on the DNL steatosis pathway and intracellular lipid accumulation compared to resmetirom, highlighting their potential as potent therapeutic candidates for MASLD.\u003c/p\u003e\u003cp\u003e\u003cb\u003eJNK-IN-5A derivatives exhibit distinct transcriptional profiles\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo investigate the transcriptional differences induced by JNK-IN-5A and its novel derivatives, we performed global RNA sequencing (RNA-seq) analysis on treated HepG2 cells. Principal Component Analysis (PCA) revealed three distinct gene expression clusters corresponding to the treatment groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). SET-130 clustered closely with JNK-IN-5A, whereas SET-151, SET-152, and SET-162 formed a separate, tightly grouped cluster. All treatment groups were distinct from the untreated control group.\u003c/p\u003e\u003cp\u003eIn terms of global transcriptional impact, SET-151, SET-152, and SET-162 induced substantially higher numbers of differentially expressed genes (DEGs) compared to SET-130 and JNK-IN-5A (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). Jaccard index analysis of the DEGs further confirmed that SET-151, SET-152, and SET-162 shared a high degree of similarity in both upregulated and downregulated gene sets (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC), suggesting a consistent and distinct mechanism of action among these three derivatives. To assess the functional consequences of these transcriptional changes, we first examined the expression of a previously defined MASLD-associated gene module (n\u0026thinsp;=\u0026thinsp;40) \u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. All compounds, including JNK-IN-5A and its derivatives, significantly downregulated the expression of genes within this MASLD module (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). Notably, SET-151, SET-152, and SET-162 produced a more pronounced downregulation effect, with a marked reduction in PKLR expression.\u003c/p\u003e\u003cp\u003eGene Set Overrepresentation Analysis (GSOA) revealed that the downregulated genes in all treatment groups were enriched in pathways related to lipid metabolism and glycolysis. Importantly, SET-151, SET-152, and SET-162 uniquely suppressed genes associated with fatty acid metabolism and primary bile acid biosynthesis, pathways highly relevant to MASLD pathogenesis (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE). To further explore the clinical relevance of these findings, we retrieved MASLD- and MASH-associated genes from the DisGeNET database (773 and 316 genes, respectively), \u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e and performed Gene Set Enrichment Analysis (GSEA). The results demonstrated that all treatment groups reduced the enrichment scores of MASLD-associated genes; however, SET-151, SET-152, and SET-162 were significantly more effective in this regard (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF). Interestingly, a reduction in the enrichment scores of MASH-related genes was observed exclusively in cells treated with SET-151, SET-152, and SET-162, but not in cells treated with JNK-IN-5A or SET-130 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF). Collectively, these transcriptomic analyses demonstrate that SET-151, SET-152, and SET-162 exhibit distinct and stronger transcriptional modulation compared to the parental compound JNK-IN-5A, supporting their superior therapeutic potential for MASLD and MASH.\u003c/p\u003e\u003cp\u003e\u003cb\u003eJNK-IN-5A derivatives exhibit distinct metabolic profiles\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo comprehensively characterize the metabolic alterations induced by JNK-IN-5A and its derivatives, we performed Compass metabolic activity analysis \u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e based on global transcriptomic profiling. The analysis was conducted using the RECON3D human metabolic network model, which encompasses 10,600 reactions, 5,835 metabolites and 2,248 genes. PCA of Compass output revealed three distinct metabolic clusters corresponding to the treatment groups: (1) SET-151, SET-152, and SET-162; (2) SET-130 and JNK-IN-5A; and (3) the untreated control group (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). These results highlight the distinct metabolic states induced by the three most potent JNK-IN-5A derivatives compared to both the parental compound and the control.\u003c/p\u003e\u003cp\u003eWe next performed differential reaction activity analysis to quantify metabolic alterations between the treatment and control groups. Cohen's d effect sizes were calculated for each reaction, and significance was determined using Benjamini-Hochberg adjusted Wilcoxon rank-sum tests (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), excluding reactions classified under the \"Miscellaneous\" subsystem. The SET-151, SET-152, and SET-162 groups exhibited 9,214, 9,580, and 9,945 significantly altered reactions, respectively. In contrast, no significantly changed reactions were detected in the SET-130 and JNK-IN-5A groups. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB, reactions related to lipid metabolism, including glycolysis, cholesterol metabolism, fatty acid synthesis, and pyruvate metabolism, were significantly altered in response to SET-151, SET-152, and SET-162 treatment, compared to the control group. Notably, SET-130 and JNK-IN-5A treatments did not induce significant changes in these key metabolic pathways (Supplementary Fig.\u0026nbsp;1).\u003c/p\u003e\u003cp\u003eTo further assess disease-relevant metabolic changes, we extracted 115 MASLD-associated reactions from the Compass results. The majority of these reactions were involved in cholesterol metabolism, fatty acid synthesis, and glycolysis/gluconeogenesis pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). Remarkably, activity levels of the three pyruvate kinase isoforms encoded by the PKLR gene were significantly reduced following SET-151, SET-152, and SET-162 treatment. This finding suggests that these JNK-IN-5A derivatives effectively disrupt pyruvate kinase-regulated glycolysis, contributing to their therapeutic effects in MASLD. Collectively, these metabolic analyses demonstrate that SET-151, SET-152, and SET-162 induce distinct and robust metabolic remodelling compared to JNK-IN-5A, with pronounced inhibition of lipids and pyruvate metabolism, supporting their superior potential as therapeutic candidates for MASLD.\u003c/p\u003e\u003cp\u003e\u003cb\u003eGLP-like toxicity assessment of SET-152 in rats demonstrates its safety\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAfter testing the efficacy of SET-152 in an \u003cem\u003ein vitro\u003c/em\u003e model of steatosis and revealing its MoA based on global transcriptomics and systems biology approach, we conducted a GLP-like 7-day oral tolerability study in Wistar rats to evaluate its safety at three dose levels (30, 100, and 300 mg/kg). The study included a vehicle control group (4 males and 4 females) and three dose groups, each consisting of 3 male and 3 female rats. The oral gavage dosing was administered once daily in the morning for seven consecutive days.\u003c/p\u003e\u003cp\u003eNo clinical adverse effects were observed in any of the treated animals after 7 days. Body weight, haematology, and plasma chemistry analysis were evaluated. Treatment had no significant effect on body weight (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). While mean red cell haemoglobin concentration (MCHC) and blood urea nitrogen (BUN) levels showed statistically significant changes in the treated animals, these changes were randomly distributed across dose groups and exhibited no dose-dependent effects (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). These changes were considered incidental and unrelated to the treatment by the toxicology experts at RISE, Sweden. All other parameters remained within normal ranges, further supporting the safety of the SET-152 (Supplementary Fig.\u0026nbsp;3).\u003c/p\u003e\u003cp\u003e\u003cb\u003eEvaluation of JNK-IN-5A and SET-152 in a high-fat, high-sucrose diet-induced\u003c/b\u003e \u003cb\u003ein vivo\u003c/b\u003e \u003cb\u003eMASH model\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo further assess the therapeutic potential of JNK-IN-5A and SET-152 in rats, we developed a high-fat, high-sucrose (HFHS) diet-induced MASH animal model (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). Rats were fed the HFHS diet for 84 days (12 weeks) to induce MASH, which was confirmed using non-invasive methods, including two-dimensional (2D) shear wave elastography and magnetic resonance imaging (MRI).\u003c/p\u003e\u003cp\u003eFollowing MASH induction, rats were treated with either JNK-IN-5A or SET-152 at a dose level of 30 mg/kg via oral gavage for 42 days (6 weeks). Body weight was monitored weekly throughout the study (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). By 126 days, the MASH group exhibited a significant increase in body weight (343\u0026thinsp;\u0026plusmn;\u0026thinsp;50 g) compared to the control group (300\u0026thinsp;\u0026plusmn;\u0026thinsp;25.6 g). Notably, the JNK-IN-5A (304\u0026thinsp;\u0026plusmn;\u0026thinsp;23.9 g) and SET-152 (298\u0026thinsp;\u0026plusmn;\u0026thinsp;24.3 g) treatment groups maintained their body weights comparable to those of healthy controls.\u003c/p\u003e\u003cp\u003eLiver stiffness, a marker of fibrosis, was evaluated using 2D shear wave elastography on day 126 (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC). The MASH group exhibited significantly increased liver stiffness (36.7\u0026thinsp;\u0026plusmn;\u0026thinsp;3.5 kPa) compared to controls (25.2\u0026thinsp;\u0026plusmn;\u0026thinsp;4.3 kPa). Treatment with JNK-IN-5A (29.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4 kPa) and SET-152 (29.1\u0026thinsp;\u0026plusmn;\u0026thinsp;2.9 kPa) significantly reduced liver stiffness compared to the untreated MASH group. Hepatic fat content was assessed by MRI at day 84 (post-MASH induction) and day 126 (following treatment) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD). 42 days of treatment with JNK-IN-5A (16.8\u0026thinsp;\u0026plusmn;\u0026thinsp;10.3%) and SET-152 (15.0\u0026thinsp;\u0026plusmn;\u0026thinsp;8.5%) significantly reduced hepatic fat accumulation compared to the untreated MASH group (24.5\u0026thinsp;\u0026plusmn;\u0026thinsp;3.4%). Histopathological analyses were performed on liver tissues using haematoxylin and eosin (H\u0026amp;E) staining (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eE). Steatosis, ballooning degeneration, and lobular inflammation were scored according to standard criteria: steatosis (0\u0026ndash;3), ballooning (0\u0026ndash;2), and inflammation (0\u0026ndash;3). The MASH group displayed significant pathological features, with elevated steatosis (1.43\u0026thinsp;\u0026plusmn;\u0026thinsp;0.98), ballooning degeneration (1.29\u0026thinsp;\u0026plusmn;\u0026thinsp;0.49), and lobular inflammation (0.71\u0026thinsp;\u0026plusmn;\u0026thinsp;0.49) scores. JNK-IN-5A treatment reduced steatosis (0.86\u0026thinsp;\u0026plusmn;\u0026thinsp;0.90), ballooning degeneration (0.29\u0026thinsp;\u0026plusmn;\u0026thinsp;0.76), and eliminated lobular inflammation. Remarkably, SET-152 treatment led to a 90% reduction in steatosis (0.14\u0026thinsp;\u0026plusmn;\u0026thinsp;0.38), an 89% reduction in ballooning degeneration (0.14\u0026thinsp;\u0026plusmn;\u0026thinsp;0.38), and an 80% reduction in lobular inflammation (0.14\u0026thinsp;\u0026plusmn;\u0026thinsp;0.38).\u003c/p\u003e\u003cp\u003eBlood biochemistry analyses were conducted at the end of the 126 days study to evaluate liver function and metabolic health (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The MASH group exhibited significant elevations in serum alanine aminotransferase (ALT) and TAG levels, indicative of liver injury and dyslipidaemia. Treatment with both JNK-IN-5A and SET-152 significantly reduced ALT and TAG levels, indicating an improvement in liver damage and metabolic dysfunction. To evaluate the safety of the compound in the MASH model, we performed a micronucleus (MN) assay to assess genotoxicity and conducted haematological analyses (Table 3). No significant differences in MN frequency were observed among the control, MASH, MASH\u0026thinsp;+\u0026thinsp;JNK-IN-5A, and MASH\u0026thinsp;+\u0026thinsp;SET-152 groups, indicating no detectable genotoxicity. Similarly, haematological parameters remained within normal ranges across all groups, suggesting that neither compound induced haematological toxicity. Collectively, these findings demonstrate that both JNK-IN-5A and its derivative SET-152 effectively attenuate MASH-related pathological features, including hepatic steatosis, fibrosis, inflammation, and biochemical markers, without inducing observable toxicity in an \u003cem\u003ein vivo\u003c/em\u003e MASH model. Notably, SET-152 exhibited superior therapeutic efficacy, supporting its potential as a promising candidate for MASH treatment.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cb\u003eBlood biochemistry of liver injury and MASLD progression.\u003c/b\u003e Serum biochemical markers were measured to evaluate liver function and metabolic alterations associated with MASH induction and treatment. * p value\u0026thinsp;\u0026lt;\u0026thinsp;0.05, **p value\u0026thinsp;\u0026lt;\u0026thinsp;0.01, *** p value\u0026thinsp;\u0026lt;\u0026thinsp;0.001, # p value\u0026thinsp;\u0026lt;\u0026thinsp;0.05, ## p value\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ### p value\u0026thinsp;\u0026lt;\u0026thinsp;0.001 to MASH group.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGroups\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eControl\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMASH\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMASH+\u003c/p\u003e\u003cp\u003eJNK-IN-5A\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMASH+\u003c/p\u003e\u003cp\u003eSET152\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eALP (U/L)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e158,71\u0026thinsp;\u0026plusmn;\u0026thinsp;26,71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e104,29\u0026thinsp;\u0026plusmn;\u0026thinsp;27,35\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e93,29\u0026thinsp;\u0026plusmn;\u0026thinsp;27,67\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e\u003cp\u003e118,57\u0026thinsp;\u0026plusmn;\u0026thinsp;21,97\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAST (U/L)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e236,11\u0026thinsp;\u0026plusmn;\u0026thinsp;24,65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e262,86\u0026thinsp;\u0026plusmn;\u0026thinsp;41,5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e225,39\u0026thinsp;\u0026plusmn;\u0026thinsp;90,87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e\u003cp\u003e216,6\u0026thinsp;\u0026plusmn;\u0026thinsp;40,84\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eALT (U/L)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e52,01\u0026thinsp;\u0026plusmn;\u0026thinsp;17,5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e118,63\u0026thinsp;\u0026plusmn;\u0026thinsp;32,39\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e32,68\u0026thinsp;\u0026plusmn;\u0026thinsp;6,31\u003csup\u003e###\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e\u003cp\u003e38,37\u0026thinsp;\u0026plusmn;\u0026thinsp;15,65\u003csup\u003e###\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLDH (U/L)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e1951,71\u0026thinsp;\u0026plusmn;\u0026thinsp;485,58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e2523,29\u0026thinsp;\u0026plusmn;\u0026thinsp;515,64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e1623,67\u0026thinsp;\u0026plusmn;\u0026thinsp;541,00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e\u003cp\u003e2212,83\u0026thinsp;\u0026plusmn;\u0026thinsp;389,82\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTriglyceride (mg/dL)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e81,71\u0026thinsp;\u0026plusmn;\u0026thinsp;18,22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e184,43\u0026thinsp;\u0026plusmn;\u0026thinsp;53,89\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e108,43\u0026thinsp;\u0026plusmn;\u0026thinsp;31,66\u003csup\u003e##\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e\u003cp\u003e96,14\u0026thinsp;\u0026plusmn;\u0026thinsp;37,73\u003csup\u003e###\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTotal Cholesterol (mg/dL)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e61,57\u0026thinsp;\u0026plusmn;\u0026thinsp;9,64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e76,86\u0026thinsp;\u0026plusmn;\u0026thinsp;4,91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e78,86\u0026thinsp;\u0026plusmn;\u0026thinsp;19,80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e\u003cp\u003e54,86\u0026thinsp;\u0026plusmn;\u0026thinsp;8,30\u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHDL Cholesterol (mg/dL)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e41,00\u0026thinsp;\u0026plusmn;\u0026thinsp;6,51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e38,86\u0026thinsp;\u0026plusmn;\u0026thinsp;6,31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e42,14\u0026thinsp;\u0026plusmn;\u0026thinsp;13,12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e\u003cp\u003e36,71\u0026thinsp;\u0026plusmn;\u0026thinsp;8,18\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLDL Cholesterol (mg/dL)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e10,57\u0026thinsp;\u0026plusmn;\u0026thinsp;2,51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e12,86\u0026thinsp;\u0026plusmn;\u0026thinsp;3,18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e11,29\u0026thinsp;\u0026plusmn;\u0026thinsp;2,21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e\u003cp\u003e8,43\u0026thinsp;\u0026plusmn;\u0026thinsp;2,44\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTotal Protein (g/dL)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e7,21\u0026thinsp;\u0026plusmn;\u0026thinsp;0,50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e7,56\u0026thinsp;\u0026plusmn;\u0026thinsp;0,38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e8,16\u0026thinsp;\u0026plusmn;\u0026thinsp;0,99\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e\u003cp\u003e7,54\u0026thinsp;\u0026plusmn;\u0026thinsp;0,54\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAlbumin (g/dL)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e3,20\u0026thinsp;\u0026plusmn;\u0026thinsp;0,21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e3,63\u0026thinsp;\u0026plusmn;\u0026thinsp;0,22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e3,81\u0026thinsp;\u0026plusmn;\u0026thinsp;0,45\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e\u003cp\u003e3,49\u0026thinsp;\u0026plusmn;\u0026thinsp;0,30\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGlucose (mg/dL)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e145,57\u0026thinsp;\u0026plusmn;\u0026thinsp;31,65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e205,00\u0026thinsp;\u0026plusmn;\u0026thinsp;88,24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e208,43\u0026thinsp;\u0026plusmn;\u0026thinsp;64,02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e\u003cp\u003e228,57\u0026thinsp;\u0026plusmn;\u0026thinsp;165,46\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTotal Bilirubin (mg/dL)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e0,09\u0026thinsp;\u0026plusmn;\u0026thinsp;0,05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e0,11\u0026thinsp;\u0026plusmn;\u0026thinsp;0,07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e0,10\u0026thinsp;\u0026plusmn;\u0026thinsp;0,08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e\u003cp\u003e0,10\u0026thinsp;\u0026plusmn;\u0026thinsp;0,05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCreatine (mg/dL)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e0,44\u0026thinsp;\u0026plusmn;\u0026thinsp;0,03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e0,53\u0026thinsp;\u0026plusmn;\u0026thinsp;0,14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e0,53\u0026thinsp;\u0026plusmn;\u0026thinsp;0,12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e\u003cp\u003e0,43\u0026thinsp;\u0026plusmn;\u0026thinsp;0,16\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eUric Acid (mg/dL)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e0,98\u0026thinsp;\u0026plusmn;\u0026thinsp;0,35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e1,33\u0026thinsp;\u0026plusmn;\u0026thinsp;0,24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e1,61\u0026thinsp;\u0026plusmn;\u0026thinsp;0,93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e\u003cp\u003e1,35\u0026thinsp;\u0026plusmn;\u0026thinsp;0,45\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBlood haematology following MASH induction and treatment.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGroups\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eControl\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMASH\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMASH+\u003c/p\u003e\u003cp\u003eJNK-IN-5A\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMASH+\u003c/p\u003e\u003cp\u003eSET152\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePCT\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e(%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e0,68\u0026thinsp;\u0026plusmn;\u0026thinsp;0,12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e0,71\u0026thinsp;\u0026plusmn;\u0026thinsp;0,10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e0,77\u0026thinsp;\u0026plusmn;\u0026thinsp;0,14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e\u003cp\u003e0,72\u0026thinsp;\u0026plusmn;\u0026thinsp;0,16\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePLT\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e(10^3/\u0026micro;L)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e845,57\u0026thinsp;\u0026plusmn;\u0026thinsp;158,62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e871,29\u0026thinsp;\u0026plusmn;\u0026thinsp;128,53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e947,71\u0026thinsp;\u0026plusmn;\u0026thinsp;123,89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e\u003cp\u003e904,86\u0026thinsp;\u0026plusmn;\u0026thinsp;140,16\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMCHC\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e(g/dL)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e28,24\u0026thinsp;\u0026plusmn;\u0026thinsp;0,96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e28,97\u0026thinsp;\u0026plusmn;\u0026thinsp;0,69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e29,27\u0026thinsp;\u0026plusmn;\u0026thinsp;1,14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e\u003cp\u003e29,71\u0026thinsp;\u0026plusmn;\u0026thinsp;1,11\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMCH\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e(fL)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e17,41\u0026thinsp;\u0026plusmn;\u0026thinsp;0,60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e17,84\u0026thinsp;\u0026plusmn;\u0026thinsp;0,59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e18,04\u0026thinsp;\u0026plusmn;\u0026thinsp;0,48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e\u003cp\u003e17,81\u0026thinsp;\u0026plusmn;\u0026thinsp;0,60\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMCV\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e(fL)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e61,67\u0026thinsp;\u0026plusmn;\u0026thinsp;2,07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e61,51\u0026thinsp;\u0026plusmn;\u0026thinsp;1,66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e61,69\u0026thinsp;\u0026plusmn;\u0026thinsp;2,87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e\u003cp\u003e60,11\u0026thinsp;\u0026plusmn;\u0026thinsp;2,25\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHCT\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e(%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e54,40\u0026thinsp;\u0026plusmn;\u0026thinsp;2,67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e55,51\u0026thinsp;\u0026plusmn;\u0026thinsp;2,31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e55,41\u0026thinsp;\u0026plusmn;\u0026thinsp;5,42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e\u003cp\u003e51,99\u0026thinsp;\u0026plusmn;\u0026thinsp;3,93\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHGB\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e(g/dL)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e15,19\u0026thinsp;\u0026plusmn;\u0026thinsp;1,27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e16,09\u0026thinsp;\u0026plusmn;\u0026thinsp;0,76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e16,20\u0026thinsp;\u0026plusmn;\u0026thinsp;1,51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e\u003cp\u003e15,40\u0026thinsp;\u0026plusmn;\u0026thinsp;1,27\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRBC\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e(10^6/\u0026micro;L)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e8,83\u0026thinsp;\u0026plusmn;\u0026thinsp;0,60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e9,04\u0026thinsp;\u0026plusmn;\u0026thinsp;0,52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e8,98\u0026thinsp;\u0026plusmn;\u0026thinsp;0,65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e\u003cp\u003e8,66\u0026thinsp;\u0026plusmn;\u0026thinsp;0,73\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eWBC\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e(10^3 /\u0026micro;L)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e5,81\u0026thinsp;\u0026plusmn;\u0026thinsp;2,27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e4,70\u0026thinsp;\u0026plusmn;\u0026thinsp;1,50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e4,27\u0026thinsp;\u0026plusmn;\u0026thinsp;1,67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e\u003cp\u003e5,75\u0026thinsp;\u0026plusmn;\u0026thinsp;3,07\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eHaematological analysis was performed to evaluate systemic effects of MASH and subsequent treatment with JNK-IN-5A or SET152. Parameters include platelet count (PLT), procalcitonin (PCT), mean corpuscular haemoglobin concentration (MCHC), mean corpuscular haemoglobin (MCH), mean corpuscular volume (MCV), haematocrit (HCT), haemoglobin (HGB), red blood cell count (RBC), and white blood cell count (WBC).\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eTable 3. Micronucleus assay.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cimg src=\"https://myfiles.space/user_files/127393_c7e80a1c9bb65875/127393_custom_files/img1758827014.png\"\u003e\u003c/strong\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eFemur bones marrow micronucleus ratio was calculated.\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThe dysregulation of hepatic lipid metabolism, particularly increased DNL, is a central feature of MASLD and its progressive form, MASH. Multiple enzymes in the DNL pathway have been identified as therapeutic targets, with several candidates showing promising results in clinical and preclinical studies. Denifanstat (TVB-2640), a FASN inhibitor currently in a Phase 2b clinical trial, suppresses FASN activity and reduces hepatic TAG accumulation by inhibiting DNL. In addition to its anti-steatotic effects, Denifanstat has been shown to attenuate steatohepatitis through the deactivation of hepatic stellate cells \u003csup\u003e\u003cspan additionalcitationids=\"CR39\" citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e–\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e and has demonstrated anti-cancer activity in models of lung carcinoma, breast cancer, astrocytoma, and colon cancer \u003csup\u003e\u003cspan additionalcitationids=\"CR42 CR43\" citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e–\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. Similarly, Firsocostat, an ACACA inhibitor that has completed a Phase 2 clinical trial, effectively reduces hepatic DNL \u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e and improves liver fibrosis \u003csup\u003e\u003cspan additionalcitationids=\"CR47\" citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e–\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. Several preclinical ACACA inhibitors, including CP-640186 \u003csup\u003e49,50\u003c/sup\u003e, Soraphen A \u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e,\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e, and TOFA \u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e, have also been reported to reduce weight gain, hepatic steatosis, and inflammation, while exhibiting anti-cancer properties. The anti-cancer effects of the FASN and ACACA-targeting molecules are attributed mainly to the suppression of lipid synthesis, a metabolic pathway essential for both energy storage and the production of key cellular components, including membranes and signalling molecules. Importantly, lipid biosynthesis is often markedly upregulated in cancer cells to support rapid proliferation, a phenomenon associated with the Warburg effect \u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eOur study expands on this therapeutic paradigm by identifying PKL isoform, encoded by the PKLR gene, as a novel, highly disease-associated target for MASLD and MASH. Systems biology analysis, combined with global transcriptomic profiling, has identified PKLR as one of the most significantly associated genes with MASLD and HCC \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. PKLR catalyses the final step of glycolysis, linking glucose and pyruvate metabolism to the DNL pathway by providing precursors for fatty acid synthesis. Notably, the inhibition of PKLR is expected to suppress both glycolysis-derived substrate availability and downstream lipogenesis, offering a multifaceted approach to target metabolic dysregulation in MASLD.\u003c/p\u003e\u003cp\u003eIn this context, we applied a computational drug repurposing approach and identified JNK-IN-5A as a small molecule that modulates PKLR expression. Building on this, we synthesized and evaluated a series of JNK-IN-5A derivatives (SET-151, SET-152, SET-162, and SET-130), three of which (SET-151, SET-152, and SET-162) exhibited superior anti-steatotic efficacy. Our data demonstrate that these derivatives not only downregulate PKLR expression but also suppress the expression of critical enzymes in the DNL pathway (FASN, ACACA) and SCD. SCD is a well-established therapeutic target for MASLD and certain cancers \u003csup\u003e\u003cspan additionalcitationids=\"CR55 CR56 CR57\" citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e–\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e. Pharmacological inhibition of SCD has been shown to exert beneficial effects in metabolic diseases and cancer. For example, the SCD inhibitor E6446 suppresses both SCD expression and the transcription factor ATF3, leading to impaired adipogenic differentiation and reduced hepatic lipogenesis \u003csup\u003e\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e. Furthermore, Ascenzi et al. demonstrated the pivotal role of SCD as a key therapeutic target in cancer \u003csup\u003e\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e. Inhibition of SCD reduces MUFA synthesis, resulting in the accumulation of SFAs. Elevated intracellular SFA levels are known to trigger cellular stress responses, including enhanced autophagy. Excessive autophagy can, in turn, promote apoptosis, lipotoxicity, and ferroptosis. Based on these established mechanisms, we hypothesize that the marked reduction of SCD protein expression observed following treatment with JNK-IN-5A and its derivatives may contribute to the reduced cell viability seen in the HepG2 DNL steatosis model. The simultaneous downregulation of PKLR, FASN, ACACA, and SCD by our compounds offers a unique multiple-targeting strategy that disrupts both substrate supply and enzymatic execution of lipid biosynthesis.\u003c/p\u003e\u003cp\u003eFurther transcriptomic and metabolic modelling revealed that SET-151, SET-152, and SET-162 induced broad transcriptional changes beyond PKLR suppression, downregulating pathways essential for MASLD and MASH progression, including pyruvate metabolism, bile acid biosynthesis, fatty acid metabolism, and glycolysis. These transcriptomic effects were also revealed in Compass metabolic activity analysis, which demonstrated that only these three derivatives, but not JNK-IN-5A or SET-130, significantly altered reaction activities within key lipid metabolic pathways, including glycolysis and fatty acid synthesis, which are crucial biological pathways in MASLD and MASH progression\u003csup\u003e\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u003c/sup\u003e. Of particular interest, activity levels of all three pyruvate kinase isoforms were reduced, suggesting that the observed metabolic reprogramming is directly linked to PKLR downregulation and impaired glycolytic flux fuelling DNL.\u003c/p\u003e\u003cp\u003eMechanistically, the link between JNK signalling, SREBP-1 activation, and DNL regulation provides a plausible explanation for the broad inhibitory effects of JNK-IN-5A and its derivatives. Previous studies have shown that MAPK-mediated phosphorylation of SREBP-1 is critical for its activation and nuclear translocation, enabling the transcription of lipogenic genes such as FASN and ACACA \u003csup\u003e\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u003c/sup\u003e. Preventing phosphorylation by JNKs has been shown to protect against hepatic steatosis and visceral obesity in mice \u003csup\u003e\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u003c/sup\u003e. However, further studies are needed to elucidate the precise regulatory mechanisms underlying the DNL pathway inhibition mediated by JNK-IN-5A and its derivatives. Our data support this model, showing that SET-151, SET-152, and SET-162 more potently reduce SREBP-1 and ChREBP levels than JNK-IN-5A, accompanied by a more substantial reduction in downstream DNL enzymes.\u003c/p\u003e\u003cp\u003eImportantly, \u003cem\u003ein vivo\u003c/em\u003e studies using a HFHS diet-induced MASH rat model demonstrated that JNK-IN-5A and SET-152 reduced hepatic fat accumulation, liver stiffness, and key histological features of MASH, including steatosis, ballooning degeneration, and lobular inflammation. SET-152, in particular, showed more profound therapeutic effects than the reference compound, consistent with its superior \u003cem\u003ein vitro\u003c/em\u003e activity. These beneficial effects were achieved without evidence of genotoxicity or haematological toxicity, underscoring the therapeutic potential of this new chemical class. Further mechanistic studies are warranted to fully elucidate the interplay between JNK signalling, PKLR expression, and SREBP-1 regulation, and to explore the translational potential of these compounds in human clinical settings.\u003c/p\u003e\u003cp\u003eCollectively, our findings identify PKLR as a promising, previously underexplored therapeutic target for MASLD and MASH. The development of SET-152 is capable of simultaneously suppressing PKLR, FASN, ACACA, and SCD, representing a novel, multi-targeted approach to disrupt the metabolic underpinnings of hepatic steatosis and fibrosis. The superior efficacy of SET-152, as demonstrated through \u003cem\u003ein vitro\u003c/em\u003e and \u003cem\u003ein vivo\u003c/em\u003e analyses, highlights its potential as a first-in-class DNL and lipid metabolism modulating compound for MASLD and MASH therapy.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cp\u003e\u003cb\u003eCell culture and DNL steatosis induction\u003c/b\u003e\u003c/p\u003e\u003cp\u003eHepG2 wild-type cells (ATCC, ATCC HB-8065™) were purchased from the genome engineering company Synthego. Cells were maintained with RPMI 1640 (R2405, Sigma-Aldrich) supplemented with 10% fetal bovine serum (FBS, F7524, Sigma-Aldrich), 1% P/S (P4333, Sigma-Aldrich). 6x10\u003csup\u003e4\u003c/sup\u003e cells HepG2 cells were seeded into a 96-well plate format for assay, and 1x10\u003csup\u003e6\u003c/sup\u003e cells were plated into a 6-well plate for western blot and image analysis. DMEM high glucose (D0819, Sigma-Aldrich) with 10% FBS, 1% P/S supplemented with 10µg/ml insulin (I9278, Sigma-Aldrich), and 10µM T0901317 (T2320, Sigma-Aldrich) was changed to induce DNL steatosis in HepG2 cells. DNL steatosis media and compounds were changed for one week, with a 3-day, 2-day, and 2-day cycle.\u003c/p\u003e\u003cp\u003e\u003cb\u003eTriacylglycerol (TAG), Cell viability (MTT) assay, Oil Red O staining, and BODIPY™ staining\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTAG levels were quantified using the Triacylglycerol Assay Kit – Quantification (ab65336, Abcam). HepG2 cells were lysed and incubated with 50 µL of assay buffer containing 2 µL of Cholesterol Esterase/Lipase for 30 minutes at room temperature to hydrolyse TAGs. The cell lysate was then mixed with an additional 100 µL of assay buffer and centrifuged using a tabletop centrifuge. A 50 µL aliquot of the supernatant was combined with 50 µL of assay buffer containing 2 µL of Triglyceride Enzyme Mix and 2 µL of OxiRed Probe, followed by a 10-minute incubation at room temperature. Absorbance was measured at 570 nm using a microplate reader (Hidex Sense Beta Plus).\u003c/p\u003e\u003cp\u003eCell viability was assessed using the MTT (M6494, ThermoFisher) according to the manufacturer’s instructions. For Oil Red O staining, HepG2 cells were fixed with 4% formaldehyde for 30 min at room temperature. Neutral lipids were stained using the Oil Red O Staining Kit (MAK194, Sigma-Aldrich) following the manufacturer’s protocol. BODIPY™ 493/503 (D3922, Invitrogen) was used for fluorescent staining of intracellular TAGs. After fixation with 4% formaldehyde, cells were washed with PBS and incubated with 2 µM BODIPY™ 493/503 in PBS for 15 minutes in the dark. For counterstaining, 100 nM Phalloidin Alexa Fluor™ 594 (A12381, Invitrogen) in PBS was applied following PBS wash.\u003c/p\u003e\u003cp\u003e\u003cb\u003eWestern blot analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eHepG2 cells were lysed with CelLytic M (C2978, Sigma-Aldrich) buffer. 20µg protein lysate was prepared with 2x Laemmli Sample Buffer (1610737, Biorad). SDS PAGE was conducted using Mini-PROTEAN® TGX™ Precast Gels (Bio-Rad) and transferred by Trans-Blot® Turbo™ Transfer System (Bio-Rad). FASN (ab22759, Abcam), ACACA (NBP2-55439, Novus), ChREBP (ab92809, Abcam), SREBP-1C (PA1 337, Invitrogen), SCD-1 (ab236868, abcam), STAT1 (HPA000982), JNK1 (ab199380, Abcam), JNK2 (ab76125, Abcam), JNK3 (MA5-35246, Invitrogen), PKL (06653, Sigma), PKM (4053S, Cell signalling), Tyr-105 p-PKM (3827S, Cell signalling), and GAPDH (ab8245, Abcam) were blotted as a primary antibody for overnight. Secondary antibodies, Goat Anti-Rabbit HRP (ab205718) and goat anti-mouse IgG-HRP (ab67895, Abcam) were blotted for one hour. The protein band was detected with ImageQuantTMLAS 500 (29-0050-63, GE).\u003c/p\u003e\u003cp\u003e\u003cb\u003eLibrary preparation and RNA-sequencing\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe Illumina Stranded Total RNA Prep, Ligation with Ribo-Zero Plus kit was used for the construction of NGS libraries. RNA samples were sequenced with 2x100 paired-end reads by the NovaSeq 6000 system. Raw sequencing data (.bcl) was converted to FastQ with DRAGEN Software (v3.9.5). The data was delivered in FASTQ format using Illumina 1.8 quality scores.\u003c/p\u003e\u003cp\u003e\u003cb\u003eRNA-seq data pre-processing\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe fastq files were first explored by FastQC (v0.11.9) for quality control. Gene expression count data was quantified using the standard protocol of Kallisto (v0.48.0).\u003csup\u003e\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u003c/sup\u003e We retrieve the reference cDNA (was GRCh38, Ensembl release 110 for Homo sapiens) for alignment and quantification from the Ensembl website. After filtering out non-protein-coding genes and genes with an average count of less than 5, the Kallisto data was used for the downstream analysis. There were 14,444 genes for the downstream analysis.\u003c/p\u003e\u003cp\u003e\u003cb\u003eOpen Mechanism of Action (MoA)\u003c/b\u003e\u003c/p\u003e\u003cp\u003eOpen MoA \u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e was used to predict the potential MoA for compounds. Open MoA integrated network was used as the reference network. Genes with TPM values more than 1.00 were used to build the HepG2 network. MAPK9 (JNK2) was set as the starting point and FDR values of DEGs were computed to construct the weighted network. Eventually, specific weighted subnetworks were built for each of the drugs. In terms of the MoA prediction, ‘most possible path’ function was used to identify the most potential MoA between JNK2-SREBP1-c and JNK2-ChREBP, respectively.\u003c/p\u003e\u003cp\u003e\u003cb\u003eDifferential expression analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eDESeq2 R package (v1.36.0)\u003csup\u003e\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u003c/sup\u003e was used to identify the differentially expressed genes (DEGs). To better visualize the data, we adapted \u003cem\u003eapeglm\u003c/em\u003e method for effect size shrinkage\u003csup\u003e\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e\u003c/sup\u003e. Adjusted P-value \u0026lt; 0.05 was chosen as the threshold for the significance of DEGs, with log\u003csub\u003e2\u003c/sub\u003e fold change \u0026gt; 1 for up-regulated genes and log\u003csub\u003e2\u003c/sub\u003e fold change \u0026lt; -1 for down-regulated genes. The Benjamini-Hochberg (BH) correction was used for multiple testing corrections. Jaccard index was used to assess the similarity among the DEGs across various treatment groups, which is defined as the size of the intersection divided by the size of the union of two gene sets.\u003c/p\u003e\u003cp\u003e\u003cb\u003ePrincipal component analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eGene expression profiles after variance stabilizing transformation were used in Principal Component Analysis (PCA) to explore the sample distribution using the R package of pcaMethods (v1.92.0)\u003csup\u003e\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003e\u003cb\u003eGene set functional analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eGene set overrepresentation analysis (GSOA) was applied to determine whether known biological functions or processes were overrepresented in the DEGs induced by different treatments. Up and down-regulated genes of different treatment groups were extracted for genes of interest and all detected genes were extracted as the background genes. Then, GSOA was applied to determine whether a list of DEGs of interest was significantly associated with specific Gene Ontology (GO) biological process terms.\u003c/p\u003e\u003cp\u003eWe also performed gene set enrichment analysis (GSEA)\u003csup\u003e\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e\u003c/sup\u003e. For this, genes were sorted by log\u003csub\u003e2\u003c/sub\u003e fold change in descending order, and disease-related genes from DisGeNET\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e were tested for their significance. The R package clusterProfiler (v4.4.4) \u003csup\u003e\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e,\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e\u003c/sup\u003ewas used for both GSOA and GSEA.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMetabolic activity analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWe used Compass, a flux balance-based algorithm for metabolic model analysis\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. Gene expression levels of all the samples were used as input. The model was created using Recon3D, which was downloaded from the BiGG Models platform\u003csup\u003e\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003e\u003cb\u003eGLP-like toxicity study in rats\u003c/b\u003e\u003c/p\u003e\u003cp\u003eA 7-day oral toxicity study was conducted in Wistar rats (supplier: Envigo, Venray, Netherlands) to preliminarily assess the tolerability of SET-152 at dose levels of 30, 100, and 300 mg/kg body weight. The study included a vehicle control group (4 males and 4 females) and three SET-152-treated groups, each consisting of 3 male and 3 female rats. The vehicle consisted of 1.5% (w/w) hydroxypropyl methylcellulose (HPMC) and 1.5% (w/w) polysorbate 80 (PS80) in 10 mM phosphate-buffered saline (PBS, pH 7), administered at a dose volume of 5 mL/kg. Dosing was performed once daily in the morning for seven consecutive days.\u003c/p\u003e\u003cp\u003e0.5 mL of blood was collected into K₂EDTA tubes per animal for haematology analysis, and 0.6 mL of blood was collected into lithium heparin tubes for plasma chemistry analysis. All samples were analysed within 60 minutes of collection. The Exigo haematology analyser was calibrated against a reference sample provided by the manufacturer, and a complete control sample analysis cycle was performed before analysis. All animal procedures and ethical reviews were performed in accordance with the 2010/63/EU Directive on the protection of animals used for biomedical research.\u003c/p\u003e\u003cp\u003e\u003cb\u003eAnimal management and MASH induction\u003c/b\u003e \u003cb\u003ein vivo\u003c/b\u003e\u003c/p\u003e\u003cp\u003e Sprague Dawley rats (age: 6–8 weeks; weight: 250 ± 17 g) were obtained from Experimental Research and Application Center of Atatürk University (ATADEM) with ethical approval. A total of four groups were established. The control group consisted of eight rats and continued to receive standard feed used under normal conditions throughout the study (Bayramoğlu Yem, Erzurum, TURKIYE). The remaining three groups, in which the MASH model was to be induced, were fed a specialized diet (MD.88137), the composition of high sucrose (34% by weight), high fat (21% by weight; 42% kcal from fat), cholesterol (0.2% total cholesterol). All groups had access to clean drinking water (Doyum Su, Erzurum, TURKIYE). The detailed composition of the diet is provided in the Supplementary Figure S3. All experimental groups were weighed weekly.\u003c/p\u003e\u003cp\u003e\u003cb\u003e2D Shear Wave Elastography and Magnetic Resonance Imaging (MRI)\u003c/b\u003e\u003c/p\u003e\u003cp\u003eUltrasonography and 2D Shear Wave Elastography (2D-SWE) imaging were performed using the EPIQ Elite (Philips, Amsterdam, the Netherlands) device by two well-experienced radiologists. Five measurements with a 1 mm ROI diameter were taken from the liver with a high-frequency eL18-4 linear probe. The average stiffness values in kilopascals (kPa) of these five measurements were recorded. For the MRI measurement, animals were anesthetized intraperitoneally while lying supine with their hind limbs extended parallel to their body. Magnetic resonance imaging (MRI) was performed using a 3 Tesla clinical scanner (Magnetom Skyra; Siemens Healthineers, Erlangen, Germany), with the rats positioned in a prone position. T1 FL2D (Fast Low-Angle Shot Two-dimensional) sequences were used to obtain in-phase and out-of-phase images for evaluating and calculating fat fraction. The T1 FL2D sequence parameters included a TR (repetition time) of 110 ms, TE (echo time) of 1.40 ms, voxel size of 1.1×1.1 ×3.5 mm, 30 slices with a slice thickness of 3.5 mm. Manual measurements were conducted to calculate fat fraction. Signal intensities were detected with a 0.11 mm² ROI diameter from liver and with a 0.3–0.5 mm² ROI diameter from spleen, both on in-phase and out-of-phase images. Fat percentage was measured by 100 × (liver SIIP/spleen SIIP - liver SIOP/spleen SIOP) / (2 × liver SIIP/spleen SIIP)\u003c/p\u003e\u003cp\u003e\u003cb\u003eHistopathological analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAt the end of the experiment, tissue samples were fixed in 10% neutral buffered formaldehyde for 48 hours and processed using standard histological techniques. Following paraffin embedding, 4 µm-thick sections were prepared and stained with hematoxylin and eosin (H\u0026amp;E) for histopathological evaluation under a light microscope (Nikon Eclipse Ci). Histopathological assessment was performed based on characteristic morphological features. The severity of hepatic steatosis was graded separately, considering both histological activity and fibrosis stage. The activity level was assessed using the MAFLD Activity Score (NAS), calculated as the sum of three histological components—steatosis (0–3), lobular inflammation (0–3), and hepatocellular ballooning (0–2)—yielding a total score ranging from 0 to 8, as described by Kleiner et al \u003csup\u003e\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMicronucleus assay in bone marrow smears\u003c/b\u003e\u003c/p\u003e\u003cp\u003eFemur bones were isolated from sacrificed rats, and surrounding muscle tissue was removed. Bone marrow was flushed using a sterile syringe with 0.5 mL FBS and 0.5 mL DMEM into Falcon tubes. The suspension was centrifuged at 2000 rpm for 5 min, and the supernatant was discarded. The pellet was resuspended in a drop of FBS, homogenized, and smeared onto slides. After air-drying, slides were fixed in absolute methanol for 10 min. Staining was performed using a commercial kit (ChemBio Laboratory Research, Turkiye) following the manufacturer’s instructions. Slides were examined under a light microscope (100× oil immersion), and 1000 polychromatic erythrocytes (PCEs) per slide were scored to determine the frequency of micronuclei (MN).\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAUTHOR CONTRIBUTIONS\u003c/h2\u003e\u003cp\u003eConceptualization, W.K. and A.M.; Methodology, W.K., M.L., X.L., S.\u0026Ouml;., E.Y., M.S., C.B., F.C.C., S.A.A., A.T.A., F.A., H.J., H.Y., S.I., J.S., S.A., B.B., J.B., M.U., C.Z., H.T.; Writing \u0026ndash; Original Draft, W.K., M.L., X.L.; Writing \u0026ndash; Review \u0026amp; Editing, W.K., M.L., A.M.; Supervision, A.M., C.Z., M.U., and H.T.\u003c/p\u003e\u003ch2\u003eACKNOWLEDGMENTS\u003c/h2\u003e\u003cp\u003eThis work was financially supported by ScandiEdge Therapeutics and Knut and Alice Wallenberg Foundation. M.Z.L. is being sponsored in her doctoral study by the China Scholarship Council (Grant No.202208440189)\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMardinoglu A, Palsson B (2025) \u0026Oslash;. Genome-scale models in human metabologenomics. \u003cem\u003eNat. Rev. Genet.\u003c/em\u003e 26, 123\u0026ndash;140\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePaschos P, Paletas K (2009) Non alcoholic fatty liver disease and metabolic syndrome. Hippokratia 13:9\u0026ndash;19\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSinton MC (2020) \u003cem\u003eMacrovesicular Steatosis in Nonalcoholic Fatty Liver Disease Is a Consequence of Purine Nucleotide Cycle Driven Fumarate Accumulation\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://biorxiv.org/lookup/doi/\u003c/span\u003e\u003cspan address=\"http://biorxiv.org/lookup/doi/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1101/2020.06.23.166728\u003c/span\u003e\u003cspan address=\"10.1101/2020.06.23.166728\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e doi:10.1101/2020.06.23.166728\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDhamija E, Paul SB, Kedia S (2019) Non-alcoholic fatty liver disease associated with hepatocellular carcinoma: An increasing concern. Indian J Med Res 149:9\u0026ndash;17\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTesfay M, Goldkamp WJ, Neuschwander-Tetri BA (2018) NASH: The Emerging Most Common Form of Chronic Liver Disease. Mo Med 115:225\u0026ndash;229\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSheka AC et al (2020) Nonalcoholic Steatohepatitis: A Review. \u003cem\u003eJAMA\u003c/em\u003e 323, 1175\u0026ndash;1183\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRiazi K et al (2022) The prevalence and incidence of NAFLD worldwide: a systematic review and meta-analysis. Lancet Gastroenterol Hepatol 7:851\u0026ndash;861\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFDA approves (2024) first MASH drug. Nat Biotechnol 42:540\u0026ndash;540\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBradbury MW (2006) Lipid Metabolism and Liver Inflammation. I. Hepatic fatty acid uptake: possible role in steatosis. Am J Physiol -Gastrointest Liver Physiol 290:G194\u0026ndash;G198\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBrowning JD, Horton JD (2004) Molecular mediators of hepatic steatosis and liver injury. J Clin Invest 114:147\u0026ndash;152\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSanders FWB, Griffin JL (2016) \u003cem\u003eDe novo\u003c/em\u003e lipogenesis in the liver in health and disease: more than just a shunting yard for glucose. Biol Rev 91:452\u0026ndash;468\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCanbay A, Bechmann L, Gerken G (2007) Lipid Metabolism in the Liver. Z F\u0026uuml;r Gastroenterol 45:35\u0026ndash;41\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGriffett K, Burris TP (2023) Development of LXR inverse agonists to treat MAFLD, NASH, and other metabolic diseases. Front Med 10:1102469\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eShen Q et al (2011) Liver X receptor-retinoid X receptor (LXR-RXR) heterodimer cistrome reveals coordination of LXR and AP1 signaling in keratinocytes. J Biol Chem 286:14554\u0026ndash;14563\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGriffett K et al (2015) The LXR inverse agonist SR9238 suppresses fibrosis in a model of non-alcoholic steatohepatitis. Mol Metab 4:353\u0026ndash;357\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHarrison SA et al (2023) Resmetirom for nonalcoholic fatty liver disease: a randomized, double-blind, placebo-controlled phase 3 trial. Nat Med 29:2919\u0026ndash;2928\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKelly MJ et al (2014) Discovery of 2-[3,5-dichloro-4-(5-isopropyl-6-oxo-1,6-dihydropyridazin-3-yloxy)phenyl]-3,5-dioxo-2,3,4,5-tetrahydro[1,2,4]triazine-6-carbonitrile (MGL-3196), a Highly Selective Thyroid Hormone Receptor β agonist in clinical trials for the treatment of dyslipidemia. J Med Chem 57:3912\u0026ndash;3923\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKannt A et al (2021) Activation of thyroid hormone receptor-β improved disease activity and metabolism independent of body weight in a mouse model of non-alcoholic steatohepatitis and fibrosis. Br J Pharmacol 178:2412\u0026ndash;2423\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHatziagelaki E, Paschou SA, Sch\u0026ouml;n M, Psaltopoulou T, Roden M (2022) NAFLD and thyroid function: pathophysiological and therapeutic considerations. Trends Endocrinol Metab 33:755\u0026ndash;768\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhou J et al (2022) Thyroid Hormone Decreases Hepatic Steatosis, Inflammation, and Fibrosis in a Dietary Mouse Model of Nonalcoholic Steatohepatitis. Thyroid Off J Am Thyroid Assoc 32:725\u0026ndash;738\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKarim G, Department of Medicine MS, Israel N, York NY, Bansal USA (2023) Division of Liver Diseases, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA. Resmetirom: An Orally Administered, Small-molecule, Liver-directed, β-selective THR Agonist for the Treatment of Non-alcoholic Fatty Liver Disease and Non-alcoholic Steatohepatitis. Eur Endocrinol 19:60\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLee S et al (2017) Network analyses identify liver-specific targets for treating liver diseases. Mol Syst Biol 13:938\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiu Z et al (2019) Pyruvate kinase L/R is a regulator of lipid metabolism and mitochondrial function. Metab Eng 52:263\u0026ndash;272\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYuan M et al (2025) Targeting PKLR in liver diseases. Trends Endocrinol Metab S1043276025000542. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.tem.2025.03.009\u003c/span\u003e\u003cspan address=\"10.1016/j.tem.2025.03.009\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKim W et al (2023) Characterization of an in vitro steatosis model simulating activated de novo lipogenesis in MAFLD patients. iScience 26:107727\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang C et al (2022) Discovery of therapeutic agents targeting PKLR for NAFLD using drug repositioning. eBioMedicine 83:104214\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHarrison SA et al (2024) A Phase 3, Randomized, Controlled Trial of Resmetirom in NASH with Liver Fibrosis. N Engl J Med 390:497\u0026ndash;509\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eIqbal S et al (2024) Design and synthesis of novel JNK inhibitors targeting liver pyruvate kinase for the treatment of non-alcoholic fatty liver disease and hepatocellular carcinoma. Bioorg Chem 147:107425\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eIto M et al (2013) A novel JNK2/SREBP-1c pathway involved in insulin-induced fatty acid synthesis in human adipocytes. J Lipid Res 54:1531\u0026ndash;1540\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiao X et al (2023) Open MoA: Revealing the Mechanism of Action (MoA) based on Network Topology and Hierarchy. \u003cem\u003eBioinformatics\u003c/em\u003e btad666 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/bioinformatics/btad666\u003c/span\u003e\u003cspan address=\"10.1093/bioinformatics/btad666\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGrohmann M et al (2018) Obesity Drives STAT-1-Dependent NASH and STAT-3-Dependent HCC. Cell 175:1289\u0026ndash;1306e20\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLu Q et al (2021) Metabolic Changes of Hepatocytes in NAFLD. Front Physiol 12:710420\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYang W, Lu Z (2013) Nuclear PKM2 regulates the Warburg effect. Cell Cycle 12:3343\u0026ndash;3347\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHitosugi T et al (2009) Tyrosine Phosphorylation Inhibits PKM2 to Promote the Warburg Effect and Tumor Growth. Sci Signal 2\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYang H et al (2021) A network-based approach reveals the dysregulated transcriptional regulation in non-alcoholic fatty liver disease. iScience 24:103222\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePi\u0026ntilde;ero J et al (2015) DisGeNET: a discovery platform for the dynamical exploration of human diseases and their genes. \u003cem\u003eDatabase J. Biol. Databases Curation\u003c/em\u003e bav028 (2015)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWagner A et al (2021) Metabolic modeling of single Th17 cells reveals regulators of autoimmunity. Cell 184:4168\u0026ndash;4185e21\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLoomba R et al (2021) TVB-2640 (FASN Inhibitor) for the Treatment of Nonalcoholic Steatohepatitis: FASCINATE-1, a Randomized, Placebo-Controlled Phase 2a Trial. Gastroenterology 161:1475\u0026ndash;1486\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSyed-Abdul MM et al (2020) Fatty Acid Synthase Inhibitor TVB-2640 Reduces Hepatic de Novo Lipogenesis in Males With Metabolic Abnormalities. Hepatol Baltim Md 72:103\u0026ndash;118\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eO\u0026rsquo;Farrell M et al (2022) FASN inhibition targets multiple drivers of NASH by reducing steatosis, inflammation and fibrosis in preclinical models. Sci Rep 12:15661\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFries BD, Tobias F, Wang Y, Holbrook JH, Hummon AB (2023) Lipidomics Profiling Reveals Differential Alterations after FAS Inhibition in 3D Colon Cancer Cell Culture Models. J Proteome Res acs jproteome. .3c00593\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKelly W et al (2023) Phase II Investigation of TVB-2640 (Denifanstat) with Bevacizumab in Patients with First Relapse High-Grade Astrocytoma. Clin Cancer Res Off J Am Assoc Cancer Res 29:2419\u0026ndash;2425\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFalchook G et al (2021) First-in-human study of the safety, pharmacokinetics, and pharmacodynamics of first-in-class fatty acid synthase inhibitor TVB-2640 alone and with a taxane in advanced tumors. EClinicalMedicine 34:100797\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCao D et al (2022) Discovery of a mammalian FASN inhibitor against xenografts of non-small cell lung cancer and melanoma. Signal Transduct Target Ther 7:273\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKirby BJ et al (2021) Organic Anion Transporting Polypeptide Inhibition Dramatically Increases Plasma Exposure but not Pharmacodynamic Effect nor Inferred Hepatic Intracellular Exposure of Firsocostat. Clin Pharmacol Ther 109:1334\u0026ndash;1341\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLoomba R et al (2021) Combination Therapies Including Cilofexor and Firsocostat for Bridging Fibrosis and Cirrhosis Attributable to NASH. Hepatol Baltim Md 73:625\u0026ndash;643\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAlkhouri N et al (2022) Safety and efficacy of combination therapy with semaglutide, cilofexor and firsocostat in patients with non-alcoholic steatohepatitis: A randomised, open-label phase II trial. J Hepatol 77:607\u0026ndash;618\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAlkhouri N, Lawitz E, Noureddin M, DeFronzo R, Shulman GI (2020) GS-0976 (Firsocostat): an investigational liver-directed acetyl-CoA carboxylase (ACC) inhibitor for the treatment of non-alcoholic steatohepatitis (NASH). Expert Opin Investig Drugs 29:135\u0026ndash;141\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCorominas-Faja B et al (2014) Chemical inhibition of acetyl-CoA carboxylase suppresses self-renewal growth of cancer stem cells. Oncotarget 5:8306\u0026ndash;8316\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHess D, Chisholm JW, Igal RA (2010) Inhibition of stearoylCoA desaturase activity blocks cell cycle progression and induces programmed cell death in lung cancer cells. PLoS ONE 5:e11394\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSchreurs M et al (2009) Soraphen, an inhibitor of the acetyl-CoA carboxylase system, improves peripheral insulin sensitivity in mice fed a high-fat diet. Diabetes Obes Metab 11:987\u0026ndash;991\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi S et al (2013) TOFA suppresses ovarian cancer cell growth in vitro and in vivo. Mol Med Rep 8:373\u0026ndash;378\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBurns JS, Manda G (2017) Metabolic Pathways of the Warburg Effect in Health and Disease: Perspectives of Choice, Chain or Chance. Int J Mol Sci 18:2755\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSun Q et al (2024) SCD1 is the critical signaling hub to mediate metabolic diseases: Mechanism and the development of its inhibitors. Biomed Pharmacother 170:115586\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJeyakumar SM, Vajreswari A (2022) Stearoyl-CoA desaturase 1: A potential target for non-alcoholic fatty liver disease?-perspective on emerging experimental evidence. World J Hepatol 14:168\u0026ndash;179\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMin J-Y, Kim D-H (2023) Stearoyl-CoA Desaturase 1 as a Therapeutic Biomarker: Focusing on Cancer Stem Cells. Int J Mol Sci 24:8951\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSen U, Coleman C, Sen T (2023) Stearoyl coenzyme A desaturase-1: multitasker in cancer, metabolism, and ferroptosis. Trends Cancer 9:480\u0026ndash;489\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKatoh Y et al (2022) Inhibition of stearoyl-CoA desaturase 1 (SCD1) enhances the antitumor T cell response through regulating β-catenin signaling in cancer cells and ER stress in T cells and synergizes with anti-PD-1 antibody. J Immunother Cancer 10:e004616\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang W et al (2023) Identification of novel SCD1 inhibitor alleviates nonalcoholic fatty liver disease: critical role of liver-adipose axis. Cell Commun Signal 21:268\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAscenzi F et al (2021) SCD1, autophagy and cancer: implications for therapy. J Exp Clin Cancer Res 40:265\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHeeren J, Scheja L (2021) Metabolic-associated fatty liver disease and lipoprotein metabolism. Mol Metab 50:101238\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKotzka J et al (2011) Identification of a gene variant in the master regulator of lipid metabolism SREBP-1 in a family with a novel form of severe combined hypolipidemia. Atherosclerosis 218:134\u0026ndash;143\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKotzka J et al (2012) Preventing phosphorylation of sterol regulatory element-binding protein 1a by MAP-kinases protects mice from fatty liver and visceral obesity. PLoS ONE 7:e32609\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBray NL, Pimentel H, Melsted P, Pachter L (2016) Near-optimal probabilistic RNA-seq quantification. Nat Biotechnol 34:525\u0026ndash;527\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLove MI, Huber W, Anders S (2014) Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 15:550\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhu A, Ibrahim JG, Love MI (2019) Heavy-tailed prior distributions for sequence count data: removing the noise and preserving large differences. Bioinformatics 35:2084\u0026ndash;2092\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eStacklies W, Redestig H, Scholz M, Walther D, Selbig J (2007) pcaMethods\u0026ndash;a bioconductor package providing PCA methods for incomplete data. Bioinforma Oxf Engl 23:1164\u0026ndash;1167\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSubramanian A et al (2005) Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. \u003cem\u003eProc. Natl. Acad. Sci.\u003c/em\u003e 102, 15545\u0026ndash;15550\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWu T et al (2021) clusterProfiler 4.0: A universal enrichment tool for interpreting omics data. Innov Camb Mass 2:100141\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYu G, Wang L-G, Han Y, He Q-Y (2012) clusterProfiler: an R package for comparing biological themes among gene clusters. Omics J Integr Biol 16:284\u0026ndash;287\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKing ZA et al (2016) BiGG Models: A platform for integrating, standardizing and sharing genome-scale models. Nucleic Acids Res 44:D515\u0026ndash;522\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKleiner DE et al (2005) Design and validation of a histological scoring system for nonalcoholic fatty liver disease. Hepatol Baltim Md 41:1313\u0026ndash;1321\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Pyruvate Kinase Liver and Red blood Cells (PKLR), c-Jun N-terminal kinase (JNK) family, MASLD, Small molecules, Systems Biology, In vitro \u0026 In vivo MASLD rat model","lastPublishedDoi":"10.21203/rs.3.rs-7114368/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7114368/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePyruvate kinase liver and red blood cells (PKLR) has emerged as a key gene associated with metabolic dysfunction-associated steatotic liver disease (MASLD). Through a computational drug repurposing approach, we identified JNK-IN-5A as a small molecule that significantly inhibits the c-Jun N-terminal kinase (JNK) family and suppresses PKL expression in HepG2 cells. In this study, we further evaluated JNK-IN-5A and its derivatives, including SET-151, SET-152, SET-162, and SET-130, as potential therapeutic candidates for MASLD. Building on our previously established HepG2 \u003cem\u003ede novo\u003c/em\u003e lipogenesis (DNL) steatosis model, we demonstrated that JNK-IN-5A and its derivatives markedly reduced intracellular triacylglycerol (TAG) accumulation during DNL induction. These compounds also significantly inhibited the expression of key DNL pathway proteins, including PKL, FASN, ACACA, SCD1, SREBP1-c, and ChREBP. Global transcriptomic analyses revealed that SET-151, SET-152, and SET-162 exhibited superior anti-steatotic effects compared to SET-130 and JNK-IN-5A. These three derivatives uniquely downregulated genes involved in pyruvate metabolism, bile acid synthesis, fatty acid metabolism, and glycolysis pathways, effects not observed with JNK-IN-5A alone. Additionally, Compass analysis indicated that treatment with SET-151, SET-152, and SET-162 led to significant alterations in metabolic reactions related to lipid metabolism, whereas JNK-IN-5A showed minimal impact. Finally, we evaluated JNK-IN-5A and SET-152 in a high-sucrose, high-fat diet-induced \u003cem\u003ein vivo\u003c/em\u003e rat model of MASLD. Both compounds significantly reduced hepatic lipid accumulation, liver stiffness, and key biochemical markers of MASLD. Collectively, our findings identified SET-152 as a promising drug candidate for the treatment of MASLD.\u003c/p\u003e","manuscriptTitle":"Targeting PKLR and lipogenic enzymes through JNK inhibition to develop a therapeutic strategy for MASLD and MASH","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-25 19:08:19","doi":"10.21203/rs.3.rs-7114368/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"communications-biology","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"commsbio","sideBox":"Learn more about [Communications Biology](http://www.nature.com/commsbio/)","snPcode":"","submissionUrl":"","title":"Communications Biology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Communications Series","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"22db2cc6-2fc6-49b5-9178-427acae708a6","owner":[],"postedDate":"September 25th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":53104820,"name":"Biological sciences/Chemical biology/Metabolic pathways"},{"id":53104821,"name":"Biological sciences/Drug discovery/Biomarkers/Predictive markers"},{"id":53104822,"name":"Biological sciences/Computational biology and bioinformatics/Cellular signalling networks"},{"id":53104823,"name":"Biological sciences/Systems biology"}],"tags":[],"updatedAt":"2025-09-25T19:08:19+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-25 19:08:19","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7114368","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7114368","identity":"rs-7114368","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}