ABSTRACT Nonalcoholic fatty liver disease (NAFLD) is a global health problem that is associated with various metabolic disorders. Telmisartan is a potential treatment for NAFLD due to its

ability to improve insulin sensitivity and decrease hepatic fat accumulation via modulation of PPARγ, and to suppress hepatic fibrosis by blocking angiotensin II receptors. However, the

underlying mechanisms of action of telmisartan have yet to be fully elucidated. In the present study, diabetic nonalcoholic steatohepatitis (NASH) mice (STAM mice) received daily

administrations of telmisartan for 6 weeks to assess the improvements in NASH. Hepatic transcriptome analyses revealed that the amelioration of NASH likely occurred through the regulation of

differentially expressed genes (DEGs), which were verified in palmitate-treated Hepa1c1c7 cell line. This transcriptome approach accompanied with cell-based molecular analyses provided the

opportunity to understand the fundamental molecular mechanisms underpinning the therapeutic effects of telmisartan, and will contribute to the establishment of a novel pharmacological

treatment for NASH patients. SIMILAR CONTENT BEING VIEWED BY OTHERS NETWORK PHARMACOLOGY-BASED INVESTIGATION OF THE PHARMACOLOGICAL MECHANISMS OF DIOSGENIN IN NONALCOHOLIC STEATOHEPATITIS

Article Open access 26 March 2025 FLUOROFENIDONE ATTENUATES CHOLINE-DEFICIENT, L-AMINO ACID-DEFINED, HIGH-FAT DIET-INDUCED METABOLIC DYSFUNCTION-ASSOCIATED STEATOHEPATITIS IN MICE Article

Open access 21 March 2025 A GUT MICROBIAL METABOLITE OF LINOLEIC ACID AMELIORATES LIVER FIBROSIS BY INHIBITING TGF-Β SIGNALING IN HEPATIC STELLATE CELLS Article Open access 03 November 2023

INTRODUCTION NAFLD is a global health problem with a prevalence of approximately 30% in Western countries1, and a rapidly increasing prevalence (with a trend towards a younger onset) in

Asian countries2. NAFLD is highly associated with metabolic disorders such as obesity, insulin resistance, type 2 diabetes mellitus, dyslipidemia, and hypertension3. Additionally, NAFLD

covers a broad spectrum of pathological abnormalities ranging from simple steatosis and NASH to advanced fibrosis and cirrhosis4. Furthermore, NASH is recognized as a significant risk factor

for hepatocellular carcinoma (HCC)5,6. A decade ago, it was proposed that NASH developed due to hepatic steatosis followed by the production of gut-derived endotoxins7. More recently, it

was proposed that numerous factors act in concert to induce NASH, including genetic predisposition, abnormal lipid metabolism, oxidative stress, lipotoxicity, mitochondrial dysfunction,

altered production of cytokines and adipokines, gut dysbiosis, and endoplasmic reticulum stress3. However, the pathogenesis of NASH has yet to be fully elucidated. Transcriptional profiling

studies with cohorts stratified based on histological liver parameters have demonstrated that several genes involved in the Wnt pathway, metabolism, cellular proliferation and extracellular

matrix (ECM) organization are dysregulated during the progression of NAFLD8,9. Additionally, an elegant study by Lefebvre _et al_.4, which investigated NASH disease activity using whole

genome profiling, revealed that gastric bypass, which is a surgical procedure that effectively improves NASH, significantly normalizes ECM homeostasis-associated genes. Thus, transcriptomic

investigations have elucidated the genetic contributors to NAFLD progression, and also provided an opportunity to establish novel pharmacological and/or medical treatment options.

Pharmacological agents, such as PPARγ activators, lipid-lowering agents, cytoprotective agents, and antioxidants have been used to treat NASH patients10. However, no optimal therapeutic

strategy has yet been established; thus, there is a need for novel NASH treatment modalities. Previous studies have suggested that the renin-angiotensin system (RAS) may play a critical role

in the progression of NAFLD, because activation of this system potentiates the accumulation of triglycerides, decreases hepatic fatty acid oxidation, alters very low-density lipoprotein

secretion, and increases _de novo_ lipogenesis in the liver11. Additionally, the RAS-mediated activation of hepatic stellate cells results in the acquisition of a myofibroblast-like

phenotype12. Taken together, these findings indicate that suppression of the RAS may be a potentially effective treatment for NAFLD. Telmisartan is an angiotensin II receptor (AGTR1)

antagonist used for the management of hypertension, which is the principle effector of RAS. Recently, it was demonstrated that telmisartan is a bifunctional molecule that activates PPARγ and

blocks angiotensin II receptors13. This unique feature allows telmisartan to improve insulin sensitivity and decrease hepatic fat accumulation via the modulation of PPARγ, as well as

suppress hepatic fibrosis by blocking angiotensin II receptors14,15. Clinical trials have shown that telmisartan improves fibrosis and the NAFLD activity score (NAS) in patients with NASH or

NAFLD, and thus has beneficial effects on fatty liver patients16,17. However, the molecular mechanisms of telmisartan, and the interaction between the RAS and PPAR, have yet to be fully

investigated. In the present study, telmisartan efficiently prevented the development of NASH in STAM mice. Additionally, hepatic transcriptomic analyses revealed that the amelioration of

NASH likely occurred via regulation of inflammatory- and fibrosis-related responses, and an integrated analysis of transcriptional and non-transcriptional genes regulated by telmisartan

identified cross-talk between angiotensin-PPAR-NFκB pathways that could contribute to the effects of telmisartan on NASH. This alternative approach to assessing the transcriptome accompanied

with the cell-based molecular analyses provided the opportunity to elucidate the underlying molecular mechanisms of the therapeutic effects of telmisartan and will contribute to the

establishment of novel pharmacological treatments for NASH patients. RESULTS TELMISARTAN-INDUCED AMELIORATION OF NASH IN STAM MICE The pharmacological effects of telmisartan were evaluated

in STAM mice from the steatosis stage (6 weeks of age) to the fibrosis stage (12 weeks of age). After 6 weeks of treatment, the bodyweights of the vehicle and telmisartan-treated mice did

not differ significantly (19.4 ± 3.2 and 19.5 ± 2.3 g, respectively; _p_ = 0.4963). In blood chemistry analyses, hypertension-related parameters including plasma triglyceride (TG) and

low-density lipoprotein (LDL) were significantly reduced (_p_ < 0.05), and high-density lipoprotein (HDL) was significantly increased (_p_ = 0.0007) by telmisartan (Table 1). The

liver/bodyweight ratios of the vehicle and telmisartan-treated mice were 8.07 ± 0.92 and 6.49 ± 0.83 g/100 g bodyweight, respectively, which indicates that telmisartan significantly reduced

relative liver weight compared to the vehicle (_p_ = 0.027; Fig. 1a). Consistent with the liver/bodyweight ratio, the liver TG levels of the vehicle and telmisartan-treated mice were 67.6 ± 

25.8 and 28.1 ± 6.2 mg/g liver, respectively, which shows that liver TG levels were significantly reduced by telmisartan compared to vehicle (_p_ = 0.0003; Fig. 1b). The relative mRNA levels

of fibrosis-related _Tgfb_ in the vehicle and telmisartan groups were 1.00 ± 0.23 and 0.72 ± 0.19, respectively, which indicates that telmisartan significantly decreased _Tgfb_ expression

(_p_ = 0.015; Fig. 1c). Histological examinations revealed that the telmisartan-treated mice exhibited reduction of liver steatosis, lobular inflammation as well as hepatocyte ballooning

compared to vehicle control. The NAS values in the vehicle and telmisartan groups were 4.71 ± 0.76 and 3.00 ± 0.82, respectively, which indicates that telmisartan significantly reduced the

NAS relative to the vehicle (_p_ = 0.0008; Fig. 1d, Supplementary Table 1). Furthermore, the percentages of Sirius red-positive areas in the vehicle and telmisartan groups were 1.07 ± 0.22

and 0.73 ± 0.26, respectively, which shows that telmisartan significantly reduced the degree of liver fibrosis compared to vehicle (_p_ = 0.01; Fig. 1e–g). Oil red O-positive areas in the

vehicle and telmisartan groups were 18.77 ± 6.81% and 6.76 ± 4.17%, respectively, which indicates that the presence of vesicular fat in telmisartan-treated liver tissues was significantly

reduced (_p_ = 0.0009; Fig. 1h–j). Taken together, these findings indicate that 6 weeks of daily treatment with telmisartan efficiently prevented fibrosis and lipid accumulation in the liver

and ameliorated NASH in STAM mice. TRANSCRIPTIONAL GENES-REGULATED BY TELMISARTAN Transcriptomic analyses of the liver tissues from randomly selected mice (three per group) were performed

to identify the differentially expressed transcripts due to telmisartan. After normalization, a total of 31,873 probes with signals common to all samples were subjected to hierarchical

clustering; the liver transcriptomes of the vehicle and telmisartan-treated groups exhibited distinct clusters (Fig. 2a, Supplementary Table 2). Overall, 69 DEGs exhibited a significant

change of at least 1.2-fold up- or down-regulation due to telmisartan compared to vehicle (_p_ < 0.05). Of these DEGs, 21 were up-regulated and 48 were down-regulated by telmisartan (Fig.

 2b, Supplementary Table 3). To validate the microarray result, the expression levels of the down-regulated genes by telmisartan were verified by quantitative RT-PCR (qRT-PCR), those

included ankyrin repeat and SOCS box containing 13 (_Asb13_), intercellular adhesion molecule 1 (_Icam1_), _Jun_, monoacylglycerol O-acyltransferase 1 (_Mogat1_), polo like kinase 3

(_Plk3_), and Serglycin (_Srgn_). qRT-PCR confirmed the consistent reduction of the mRNA levels of these genes by telmisartan compared to vehicle control in the liver tissues (Fig. 2c). The

up- and down-regulated genes were separately applied to functional enrichment using Gene Set Enrichment Analysis [GSEA; false discovery rate (FDR) _q_ < 0.05] to clarify whether the

molecular functions were activated or inhibited by telmisartan. There were no functions enriched with up-regulated genes. In the contrary, several inflammatory- and fibrosis-related

functions were down-regulated by telmisartan, included TNFα signaling via NFκB (_q_ = 4.99E-06), allograft rejection (_q_ = 1.65E-03), the IFNγ response (_q_ = 3.93E-02), and

epithelial-mesenchymal transition (EMT; _q_ = 3.94E-02; Fig. 2d, Supplementary Table 4). A total of 15 down-regulated genes were enriched in these functions (Fig. 2e), and these genes were

considered to be the differentially expressed as well as functionally enriched genes that were transcriptionally regulated by telmisartan. Subsequently, these genes were used as

transcriptional genes regulated by telmisartan to construct the regulatory network. NON-TRANSCRIPTIONAL GENES REGULATED BY TELMISARTAN The identification of transcriptional-regulated genes

due to the perturbation of non-transcription factors is challenging and could have a serious negative impact on the construction of a precise pathway and/or network for understanding the

molecular mechanisms of drugs. To identify non-transcriptional-regulated genes associated with telmisartan, 40 telmisartan-induced DEGs were queried in the Connectivity Map (CMap). CMap

analyses were conducted using the Touchstone signature dataset, which was generated by pharmacological perturbation for identification of drugs and their target genes, as well as by paired

genetic perturbation through knockdown or over-expression. As shown in Table 2, CMap analyses revealed that the telmisartan-induced DEGs were connected with irbesartan (angiotensin receptor

antagonist, connectivity score: 99.98), benazepril (angiotensin converting enzyme inhibitor, connectivity score: 99.96), clofibrate (PPAR receptor agonist, connectivity score: 99.96),

parthenolide (NFκB pathway inhibitor and adiponectin receptor agonist, connectivity score: 99.88), etomoxir (carnitine palmitoyltransferase inhibitor, connectivity score: 99.87), and

carbacyclin (PPARδ receptor activator, connectivity score: 99.85) (Fig. 3a, Supplementary Table 5). The associated protein targets of the drugs retrieved from CMap were as follows: AGTR1 and

JUN for irbesartan, ACE for benazepril, PPARα and LPL for clofibrate, IκBKB and RELA for parthenolide, CPT1A and CPT1B for etomoxir, and PPARδ for carbacyclin. Next, the paired genetic

perturbagens that were transcriptionally similar to the telmisartan-induced DEGs in CMap were investigated (Fig. 3b, Supplementary Table 6); the strongly paired genes included _FOXP3_

(connectivity score by knockdown/over-expression: 98.92/−97.31), _CCL2_ (connectivity score: 91.16/−98.32), _ADRB2_ (connectivity score: 99.73/−97.86), and _BCL10_ (connectivity score:

97.23/−96.73). This approach identified 11 target genes of pharmacological perturbagens and 10 genetic perturbagens; these genes were regarded as non-transcriptional-regulated genes by

telmisartan. Subsequently, these genes were used to construct the regulatory network of telmisartan in combination with the transcriptional-regulated genes. TELMISARTAN-INDUCED REGULATORY

NETWORK FOR IMPROVEMENT OF NASH The transcriptional and non-transcriptional genes regulated by telmisartan were assessed using STRING and a protein-protein interaction (PPI) network was

constructed. A PPI network was generated with 19 protein nodes encoded by telmisartan-regulated genes (Fig. 4a). Of the nodes in the network, six were functionally enriched in the NAFLD

pathway (_q_ = 2.81E-07), which was located at the center of this network and interconnected with the PPAR signaling pathway (_q_ = 2.81E-07), TNFα signaling pathway (_q_ = 6.18E-08), and

angiotensin signaling pathway. To understand whether this network influenced the expression of telmisartan-induced DEGs, the associations of the transcription factors in the network with the

DEGs were investigated using ChIP-X enrichment analysis (ChEA). Interestingly, PPARα, PPARδ, and RELA were significantly associated with 6, 14, and 7 telmisartan-induced DEGs, respectively

(Fig. 4b, Supplementary Table 7), which implies that these genes were down-regulated by PPARα, PPARδ, and RELA binding, respectively. Therefore, these three transcription factors appeared to

play the essential role as network regulators, exerting an influence on the telmisartan-induced STRING network. To verify whether the telmisartan modulates PPARα, PPARδ and RELA to

influence NAFLD network, the protein levels of these transcription factors and the mRNA levels of their downstream target genes were evaluated in Hepa1c1c7 cells. As shown in Fig. 5a,

telmisartan alone did not change the protein levels of PPARα, PPARδ and RELA in Hepa1c1c7 cells. In the contrary, palmitate slightly decreased PPARα and PPARδ; furthermore, telmisartan in

palmitate-treated Hepa1c1c7 cells significantly increased the levels of PPARα and PPARδ (_p_ < 0.05), and decreased RELA (_p_ < 0.01; Supplementary Fig. 1). Coincidently, the mRNA

levels of the downstream target genes of these transcription factors were inversely correlated with the protein levels of the transcription factors (Fig. 5b). The mRNA levels of _Asb13_,

_Icam1_ and _Jun_ genes, which were predicted to be down-regulated by PPARα and/or PPARδ in ChEA, were significantly increased by palmitate (_p_ < 0.05); however, as PPARα and PPARδ

increased by co-treatment of telmisartan, the mRNA levels of these genes were significantly decreased (_p_ < 0.05). In contrast, palmitate-induced increase of _Srgn_ mRNA was

significantly reduced (_p_ < 0.01) as the RELA decreased by telmisartan. Taken together, the findings of the constructed regulatory network in conjunction with the transcriptional and

non-transcriptional genes identified as being regulated by telmisartan indicate that the AGTR1-mediated angiotensin pathway interacted with the PPAR-NFκB signaling pathway, and that the

NAFLD pathway was down-regulated through PPARα, PPARδ, and RELA as transcriptional regulators to ameliorate NASH in STAM mice. DISCUSSION Activation of the PPARγ signaling pathway improves

insulin resistance, dyslipidemia, adipokine secretion, inflammation, cell proliferation and hepatic steatosis18,19. Blockade of the RAS pathway improves oxidative stress, inflammation, and

cell proliferation, and also leads to improvements in hepatic fibrosis20. Thus, modulation of the PPARγ and RAS pathways would likely confer significant advantages for NASH patients. Based

on previous research and the present findings, the bifunctional pharmacological activities of telmisartan, as an angiotensin II receptor antagonist and PPARγ partial agonist, significantly

ameliorate NAFLD activity, alter hepatic fat accumulation, and improve hepatic fibrosis. Other PPARγ agonists, such as pioglitazone, also attenuate hepatic steatosis, inflammation, and

fibrosis to a degree similar to that of telmisartan, but also affect systemic characteristics such as lipid metabolism and body weight such that rats treated with pioglitazone exhibit

increases in body weight and subcutaneous fat. In contrast, telmisartan is associated with mild loss of body weight accompanied by marked decreases in subcutaneous inguinal and epididymal

visceral fat18. These features differentiate telmisartan and pioglitazone in terms of therapeutic efficacy for NASH patients. Telmisartan also exerts dissociable effects on hepatic steatosis

and energy expenditure to those of ordinary angiotensin II receptor antagonists, such as valsartan13. These differences may be due to differences in chemical structure. Conventional

angiotensin II receptor antagonists in clinical use today are biphenyl tetrazole derivatives, whereas telmisartan is a non-tetrazole derivative that resembles pioglitazone21. This unique

structural feature appears to grant telmisartan the ability to regulate both carbohydrate and lipid metabolism, which led to improvements in fatty liver and reductions in triglyceride

levels, without weight gain, in the present study. Gene expression profiling analysis at different stages of various diseases represents a sensitive method for elucidating the molecular

processes that underlie pathological states. The reversal of NASH and fibrosis by telmisartan seen in the present study suggests that at least some of the transcriptomic alterations were

reversible, which allowed for the identification of putative target genes that may potentially be effective against pathological processes. The unsupervised hierarchical clustering of the

present hepatic transcriptome data revealed a clear dissociation between the vehicle and telmisartan treatments. Telmisartan induced subtle changes in global gene expression levels, and this

may have been due to the adaptive nature of the pathological response to gene expression4. However, it is noteworthy that telmisartan appeared to reduce the activities of essential genes

that are associated with the inflammatory response and hepatic fibrosis. Of these genes, _Icam1_ is important in the inflammatory process of livers with NASH, and thus may be a useful marker

for the diagnosis of NASH22,23. Increased expression of _Irs2_ is associated with steatohepatitis in obese individuals24 and seems to be a critical regulator of the synthesis and oxidation

of fatty acids in the livers of rats with NASH25. Additionally, _Onecut1_ and _Cd74_ were down-regulated in telmisartan-treated liver tissues but, in contrast to the present findings, these

genes were inhibited during hepatic steatosis induction26,27. Thus, whether the activities of _Onecut1_ and _Cd74_ are regulated differentially depending on the NASH induction conditions

needs to be clarified. It is well known that gene activity is regulated by transcription, RNA processing, post-translational modification, and/or PPIs. In the present study, several

telmisartan-regulated genes were identified at the transcription level and their cellular and molecular functions were shown to attenuate NASH progression. However, it was challenging to

understand the therapeutic mechanisms underlying the effects of telmisartan on NASH, and the interaction between angiotensin II receptors and the PPAR signaling pathway, using only

transcriptional-regulated genes. The target genes of transcription factors can be efficiently revealed by knockout expression profiling because transcriptional-regulated genes would be

directly regulated by perturbations in transcription factors28. However, it would be extremely difficult to investigate the activity of non-transcriptional-regulated genes by gene expression

profiling29. Thus, CMap provides a significant opportunity to elucidate disease-drug and/or drug-drug connections at the transcription level due to massive pharmacological or genetic

perturbations that, in turn, may aid in the identification of the modes of action of certain candidate drugs, and repurpose existing drugs for alternative indications30,31. Furthermore,

pharmacological or genetic perturbation expression profiles in CMap could provide insight into the transcriptional responses of genes that are regulated in a non-transcriptional manner. To

investigate this hypothesis, the transcriptional genes that were dysregulated by telmisartan were queried in CMap. Surprisingly, CMap revealed that well-known telmisartan associated

signaling molecules such as AGTR1, ACE, PPARα, and PPARδ exhibited a pharmacological connection, while other interacting molecules had a genetic connection to telmisartan-induced DEGs; these

had never been previously identified by conventional gene expression analyses. Thus, in combination with the transcriptional genes regulated by telmisartan, the telmisartan network that

ameliorated NASH in the livers of STAM mice, and which harbored the NAFLD pathway that was interconnected with the RAS and the PPAR-NFκB signaling pathways, was successfully generated.

Furthermore, retrospective ChEAs of transcription factors in this network implied that PPARα, PPARδ, and RELA likely play important role in differentially control of target gene activity

during the reversal of NASH by telmisartan. AGTR1 has been known to activate the NFκB machinery through MAPK/ERK pathway32,33. RelA, p65 subunit of NFκB, also has been known to down-regulate

the PPARα34 and PPARδ35 activity by inhibitory binding. It implies that RelA would be a master regulator of the core transcriptional circuit by telmisartan and mediate RAS-PPAR pathway.

Interestingly, PPARγ was not identified among the transcriptional and non-transcriptional genes regulated by telmisartan. PPAR isoforms display tissue-specific expressions. For example,

PPARγ is dominant in adipose tissue, whereas PPARδ is found in various tissues and has been identified in high levels in skeletal muscle36. The potential agonism of PPARγ by telmisartan was

suggested by PPRE-dependent transcription in cells that were similar to pioglitazone-treated cells14,18. However, telmisartan also induces anti-fibrotic and anti-obesity effects through

PPARδ-dependent pathways37,38 and enacts anti-hepatic fibrosis and anti-dyslipidemic effects through PPARα-dependent pathways4,39. Therefore, telmisartan appears to inhibit NASH progression

by PPARγ activation as well as by partial activation of PPARα and PPARδ through AGTR1 antagonism, resulting in down-regulation of genes related with inflammation and fibrosis in STAM mice.

There are some potential pitfalls to consider. First, STAM mice used in this study represent diabetic, male NASH in human, and it does not explain non-diabetic NASH or female NASH patients.

Long-term HFD without streptozotocin (STZ) treatment could be an alternative model for non-diabetic NASH with variable onset and characteristics to improve the clinical relevance of the

study. Moreover, STAM mice are known as hypertension insensitive and maybe inappropriate to evaluate the anti-hypertensive effects by telmisartan40; however, telmisartan effectively

controlled the strong hypertension predictors including plasma TG, LDL and HDL at dose level used in this study. Second, there was no comparison between wild type and STAM mice to evaluate

the effect of telmisartan. However, the _in vitro_ experiment with Hepa1c1c7 cells demonstrated that telmisartan-induced amelioration of NASH would be steatosis/steatochepatitis-specific and

there would be low possibility to observe the transcriptional effect of telmisartan in wild type animals. Third, CMap has limited coverage of perturbagens. Although the coverage has been

dramatically increased in the next generation CMap with L1000 platform30, it is still retrospective and novel targets which do not have matched perturbagens would be difficult to be

connected with biological states. This limitation needs to be improved by expanding the coverage of genetic perturbagens with appropriate test system. In conclusion, telmisartan efficiently

prevented the development of NASH in STAM mice. Additionally, hepatic transcriptomic analyses revealed that the amelioration of NASH possibly occurred via the regulation of inflammatory- and

fibrosis-related responses. Integrated analyses of transcriptional and non-transcriptional genes regulated by telmisartan identified cross-talk between the angiotensin-PPAR-NFκB signaling

pathways, which could have contributed to the pharmacological effects of telmisartan on NASH. This alternative transcriptomic approach accompanied with the cell-based molecular analyses

provided the opportunity to understand the fundamental molecular mechanisms underlying the therapeutic effects of telmisartan, and will contribute to the establishment of novel

pharmacological treatments for patients with NASH. METHODS ANIMAL EXPERIMENT NASH was induced in C57BL/6 J male mice, as described previously41. Briefly, at 2 days after birth, the mice

received a single subcutaneous injection of 200 μg STZ (Sigma, St. Louis, MO, USA). Then, after 4 weeks of age, they received 60 kcal% fat HFD32 chow (CLEA Japan Inc., Tokyo, Japan) _ad

libitum_ and were assigned to receive either vehicle or telmisartan. Telmisartan (Sigma) was dissolved in 0.5% (v/v) carboxymethyl cellulose (Sigma) and orally administered (5 mg/kg/day) to

the mice from 6 to 12 weeks of age (n = 7 per group). At termination, liver tissues were obtained from the mice and stored until further analysis. This study was approved by the

Institutional Animal Care and Use Committees of Seoul National University (SNU-170912-22) and was conducted in accordance with the approved guidelines. BIOCHEMICAL ANALYSIS Blood chemistry

was analyzed using automated chemistry analyzer (Hitachi, Tokyo, Japan), those included ALT, TBIL, plasma TG, TCHO, HDL, LDL, BUM, CREA, and GLU according to Park _et al_.42. Total lipid in

the liver tissues was extracted using a 2:1 chloroform:methanol solution (v/v) and the TG contents were measured with a Triglyceride E-test kit (Wako, Osaka, Japan) according to the

manufacturer’s instructions. QRT-PCR To measure the expression levels of _Tgfb_ and ribosomal protein lateral stalk subunit P0 (_36B4_) genes, total RNA was extracted from the liver tissues

using RNAiso (Takara, Tokyo, Japan) and cDNA was prepared with Moloney murine leukemia virus reverse transcriptase (Invitrogen, Carlsbad, CA, USA). cDNA was amplified with the ABI 7700

sequence‐detector system (Applied Biosystems, Foster City, CA, USA) using a set of primers and probes that corresponded to _Tgfb_ and _36B4_ (endogenous control)41. To measure the expression

levels of _Asb13_, _Icam1_, _Jun_, _Mfge_, _Mogat1_, _Plk3_, and _Srgn_ genes, total RNA from the liver tissues (six per group) or cells was extracted using a RNeasy Mini kit (Qiagen)

according to the manufacturer’s instructions. cDNA was prepared using SuperScript III Reverse Transcriptase (Invitrogen) and qRT-PCR was performed on a StepOnePlus Real-Time PCR System

(Applied Biosystems) with Power SYBR Green PCR Master Mix (Applied Biosystems). Gene expression levels were analyzed by ΔΔCT method using _Gapdh_ as an internal control. Primers referred

from PrimerBank (https://pga.mgh.harvard.edu/primerbank/index.html)43 were summarized in Supplementary Table 8. HISTOLOGICAL ANALYSES Water-soluble glycol and resin-embedded liver sections

were cut at a thickness of 5 μm, air-dried, fixed in acetone, and then stained with a hematoxylin and eosin solution (Wako). The NAS was evaluated semi-quantitatively, as described

previously44, the degree of liver fibrosis was assessed with Sirius-red staining, and the presence of vesicular fat in the liver tissues was confirmed using oil red O. MICROARRAY ANALYSES

Total RNA was extracted from the frozen liver tissues with a RNeasy mini kit (Qiagen). Following quantitative and qualitative evaluations performed with BioAnalyzer (Agilent, Santa Clara,

CA, USA), RNA samples with an RNA integrity number (RIN) ≥ 6.7 and A260/A280 values ≥ 1.88 were subjected to cDNA synthesis, performed with the GeneChip WT cDNA synthesis and amplification

kit (Applied Biosystems). Next, the cDNA was fragmented and biotin-labeled using GeneChip WT terminal labeling kit (Applied Biosystems), and approximately 5.5 μg of labeled cDNA was

hybridized to the Affymetrix GeneChip Mouse Gene 2.0 ST Array (Affymetrix, Santa Clara, CA, USA) at 45 °C for 16 h. The hybridized arrays were scanned on a GCS3000 Scanner (Affymetrix) and

all data analyses were performed with the GeneChip Command Console Software (Affymetrix). All data were normalized using the robust multi-array average (RMA) approach and hierarchical

clustering of the expressed probes was performed using GenePattern (https://genepattern.broadinstitute.org). The distance between clusters was computed with Pearson correlations and global

gene expression profiling was conducted in triplicate for the vehicle control and telmisartan treatment groups. TELMISARTAN GENE SIGNATURE Telmisartan-induced DEGs in the liver tissues were

identified using a fold change cutoff of 1.2; these genes were compared to the vehicle control group using independent _t_-tests with a _p_-value of 0.05 taken to indicate statistical

significance. Next, the telmisartan-induced DEGs were used as seeds to generate a telmisartan gene signature. First, the DEGs were analyzed using GSEA software

(http://software.broadinstitute.org/gsea)45 to determine the molecular mechanisms of action of telmisartan. Next, the telmisartan-induced DEGs were computationally overlapped with the

Molecular Signature Database (MSigDb) using a FDR _q_-value cutoff of 0.05; DEGs enriched in a certain molecular signature were regarded as transcriptional-regulated genes by telmisartan.

Next, the transcriptional connections between telmisartan-induced DEGs and chemical and genetic perturbagens were assessed with CMap (https://clue.io)30, which is a catalog of

transcriptional responses following pharmacological or genetic (knock-down by shRNA or over-expression by transgenesis) perturbations of cell lines; the pharmacological (and their target

genes) or genetic perturbagens associated with telmisartan-induced DEGs were considered non-transcriptional-regulated genes by telmisartan. These transcriptional- and

non-transcriptional-regulated gene sets were used as the telmisartan gene signature. REGULATORY NETWORKS A system-wide understanding of the cellular functions induced by telmisartan was

obtained using the STRING protein-protein association network database (https://string-db.org)46. A telmisartan gene signature generated by GSEA and CMap analyses was used to construct both

the experimental and predicted interactions of the signature molecules using a confidence level of 0.7. To verify the experimental relevance of the network, transcription factors in the

constructed network were associated with telmisartan-induced DEGs using the ChEA gene set library47, which is a comprehensive resource for targets of transcription factors in various cell

types, mammalian organisms, and microarray platforms, as determined by ChIP-seq. CELL EXPERIMENT Hepa1c1c7 murine hepatoma cells (Korean Cell Line Bank, Korea) were maintained in αMEM medium

without nucleosides (ThermoFisher Scientific, Waltham, MA, USA) with 10% FBS (ThermoFisher Scientific) at 37 °C, 5% CO2. After reaching 70% confluency, Hepa1c1c7 cells were treated with 0.2

 mM palmitate (Sigma) for 9 h to induce lipotoxicity. The concentration of palmitate was determined by cell viability assay which did not cause significant cell death. Telmisartan at 10 µM

was treated for 24 h after palmitate treatment according to Li _et al_.36. WESTERN BLOTTING Total protein collected from cells was subject to dodecyl sulfate-poly acrylamide electrophoresis,

and then transferred to nitrocellulose membranes. Membranes were incubated with anti-PPARδ, PPARα (1:500) and anti-RELA (1:250) antibodies (ThermoFisher Scientific). Expression levels of

proteins were analyzed by ImageJ (https://imagej.nih.gov/ij/) using ACTB as an internal control. STATISTICAL ANALYSES All animal data were analyzed with Student _t_-tests or Mann-Whitney U

tests depending on the homogeneity of variance of the data. All statistical analyses were performed using Prism software (ver. 7.03; GraphPad Software, Inc., San Diego, CA, USA) with

_p_-values < 0.05 considered to indicate statistical significance. All measurements are reported as means ± standard deviation (SD). DATA AVAILABILITY The dataset generated during the

current study are available in the Gene Expression Omnibus repository, https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE120937. REFERENCES * Musso, G., Gambino, R. & Cassader, M.

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_Nucleic acids research_ 44, W90–97, https://doi.org/10.1093/nar/gkw377 (2016). Article  CAS  PubMed  PubMed Central  Google Scholar  Download references ACKNOWLEDGEMENTS This study was

supported by a grant from the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI) funded by the Ministry for Health and Welfare of Korea

(HI14C1135 to C.S.C. and J.P.) and LG Chem Inc. (1403-20170093 to J.P.). AUTHOR INFORMATION AUTHORS AND AFFILIATIONS * LG Chem R&D Campus, Daejeon, Korea Jung Gyu Park & Hee Dong

Park * Graduate School of International Agricultural Technology, Seoul National University, Seoul, Korea Jong Soo Mok, Tae Sub Park & Joonghoon Park * Institute of Green Bio Science and

Technology, Seoul National University, Seoul, Korea Young In Han, Tae Sub Park & Joonghoon Park * College of pharmacy, Seoul National University, Seoul, Korea Keon Wook Kang * Korea

mouse metabolic phenotyping center, Lee Gil Ya cancer and diabetes institute, Gachon University School of Medicine, Seongnam-si, Republic of Korea Cheol Soo Choi * Endocrinology, Internal

Medicine, Gachon University Gil Medical Center, Seongnam-si, Republic of Korea Cheol Soo Choi Authors * Jung Gyu Park View author publications You can also search for this author inPubMed 

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inPubMed Google Scholar * Tae Sub Park View author publications You can also search for this author inPubMed Google Scholar * Keon Wook Kang View author publications You can also search for

this author inPubMed Google Scholar * Cheol Soo Choi View author publications You can also search for this author inPubMed Google Scholar * Hee Dong Park View author publications You can

also search for this author inPubMed Google Scholar * Joonghoon Park View author publications You can also search for this author inPubMed Google Scholar CONTRIBUTIONS J.G.P., H.D.P. and

J.P. conceived the study. J.G.P., J.S.M., Y.I.H., T.S.P., K.W.K., C.S.C. and J.P. designed and conducted the experiments. J.P. wrote the manuscript with input from other authors.

CORRESPONDING AUTHOR Correspondence to Joonghoon Park. ETHICS DECLARATIONS COMPETING INTERESTS The authors declare no competing interests. ADDITIONAL INFORMATION PUBLISHER’S NOTE: Springer

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holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. Reprints and permissions ABOUT THIS ARTICLE CITE THIS ARTICLE Park, J.G., Mok, J.S., Han, Y.I. _et

al._ Connectivity mapping of angiotensin-PPAR interactions involved in the amelioration of non-alcoholic steatohepatitis by Telmisartan. _Sci Rep_ 9, 4003 (2019).

https://doi.org/10.1038/s41598-019-40322-1 Download citation * Received: 18 July 2018 * Accepted: 30 January 2019 * Published: 08 March 2019 * DOI: https://doi.org/10.1038/s41598-019-40322-1

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