Hepatic miR-93 promotes the pathogenesis of metabolic dysfunction-associated steatotic liver disease by suppressing SIRT1

doi:10.21203/rs.3.rs-5202645/v1

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Hepatic miR-93 promotes the pathogenesis of metabolic dysfunction-associated steatotic liver disease by suppressing SIRT1

https://doi.org/10.21203/rs.3.rs-5202645/v1

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Metabolic dysfunction-associated steatotic liver disease (MASLD) represents a global health challenge with limited therapeutic options, and its molecular mechanisms remain poorly understood. Here, we identify microRNA-93 (miR-93) as a critical regulator of MASLD progression. miR-93 was markedly upregulated in liver tissues from both MASLD patients and diet-induced obese mice. miR-93 knockout (KO) mice were protected against diet-induced hepatic steatosis and fibrosis, along with improved glucose tolerance and enhanced mitochondrial function. Transcriptomic analysis revealed that miR-93 directly targets Sirtuin 1 (SIRT1), suppressing the LKB1-AMPK signaling pathway, which disrupts lipid metabolism. Notably, pharmacological inhibition of miR-93 using niacin restored SIRT1 activity, alleviating MASLD symptoms and improving metabolic function. Our findings establish miR-93 as a novel and promising therapeutic target in MASLD. The modulation of the miR-93/SIRT1 axis, particularly through niacin treatment, presents a potential therapeutic avenue for MASLD, a disease with few current treatment options.

Biological sciences/Cell biology/Mechanisms of disease

Biological sciences/Cell biology/Cell signalling/Lipid signalling

Metabolic dysfunction-associated steatotic liver disease (MASLD)

miR-93

SIRT1

lipid metabolism

niacin

Metabolic dysfunction-associated steatotic liver disease (MASLD), previously referred to as nonalcoholic fatty liver disease (NAFLD), has rapidly become the most prevalent chronic liver disease globally, affecting an estimated 25–30% of the population worldwide.¹ MASLD encompasses a spectrum of conditions ranging from simple steatosis to more advanced forms, such as metabolic dysfunction-associated steatohepatitis (MASH), hepatic fibrosis, liver cirrhosis, and hepatocellular carcinoma.^{2, 3} Additionally, MASLD is intricately linked to metabolic disorders, such as obesity, type 2 diabetes mellitus, hypertension, and dyslipidemia; and it is also recognized as an independent risk of cardiovascular disease.^4–6 Thus, it has emerged as a significant contributor to the global burden of cardiovascular disease and liver-related morbidity and mortality.⁷

MASLD pathogenesis is driven by complex metabolic reprogramming in the liver, leading to dysregulated lipid metabolism manifesting as excessive lipid accumulation, steatosis, inflammation, and fibrosis.⁸ The liver plays a central role in lipid homeostasis, encompassing processes such as cholesterol biosynthesis, de novo lipogenesis (DNL), and fatty acid oxidation (FAO).⁹ In MASLD, dysregulated hepatic cholesterol biosynthesis results in the accumulation of cholesterol species, which exacerbate lipotoxicity and promote inflammation, mitochondrial dysfunction, and hepatocyte injury.^{10, 11} Moreover, fatty acid metabolism, characterized by increased DNL and reduced FAO, further drives steatosis and oxidative stress leading to advanced liver damage.⁸ Despite substantial progress in understanding the metabolic disturbances in MASLD, the precise molecular mechanisms remain incompletely defined, and pharmacological therapies are not available.^{12, 13}

MicroRNAs (miRNAs) are short, non-coding RNA molecules that regulate post-transcriptional gene expression and are increasingly implicated as key modulators of MASLD-related metabolic pathways.¹⁴ Several miRNAs influence lipid metabolism, inflammation, and fibrosis in the liver, making them potential targets for therapeutic intervention.¹⁵ In particular, the miR-17 family has roles in cancer, CVDs, and metabolic regulation, including liver disease.¹⁶ miR-17 family members are elevated in the circulating plasma of patients with MASLD and could serve as potential biomarkers for diagnosis and prognosis.¹⁷ However, the precise contribution of the miR-17 family to the pathophysiology of MASLD, particularly its regulatory role in hepatic lipid metabolism and inflammation, remains to be fully elucidated.

In this unbiased analysis of regulatory networks and comparative evaluation of miRNA expression in patients with MASLD and healthy controls, we identified miR-93, a member of the miR-17 family, as a key regulator in MASLD progression. Our findings revealed that miR-93 plays a pivotal role in modulating lipid metabolism by targeting the SIRT1/LKB1/AMPK signaling axis, which is involved in FAO, mitochondrial function, and metabolic homeostasis.^{18, 19} Furthermore, we identified niacin (vitamin B3) as a potent inhibitor of miR-93 expression using high-throughput chemical screening. Our findings demonstrate that suppression of miR-93 represents a potential therapeutic strategy for MASLD treatment.

miR-93 is significantly upregulated in livers of human patients with MASLD and diet-induced obese mice

To investigate the involvement of miRNAs in lipid metabolism during MASLD progression, we conducted a comprehensive regulatory network analysis comparing patients with MASLD and healthy controls. Among the downregulated genes associated with the gene ontology (GO) term “Regulation of Lipid Metabolic Process” in MASLD patient samples, our analysis focused on the enrichment of miRNA target genes. Of the top-ranked miRNA families sorted by enrichment false discovery rates (FDR), we identified miR-16 and miR-17 families as prominent candidates for further exploration (Fig. 1a). Of particular interest, miR-93, a member of the miR-17 family, was significantly upregulated in the livers of patients with MASLD compared to healthy controls (Fig. 1b).

In wild-type (WT) mice, miR-93 was predominantly expressed in the liver at markedly higher levels compared to other metabolic tissues (Extended Data Fig. 1a). In assessing the clinical relevance of miR-93, in situ hybridization analysis of MASLD livers revealed a striking increase in miR-93 expression compared to healthy controls (Fig. 1c). Furthermore, hepatic miR-93 expression positively correlated with key clinical parameters of MASLD, including body mass index (BMI), aspartate aminotransferase (AST), alanine aminotransferase (ALT) levels, and the NAFLD activity score (NAS) (Fig. 1d).

We observed consistent upregulation of miR-93 across publicly available transcriptome datasets, including human MASLD specimens (GSE48452 and GSE135251) and murine models of MASLD (GSE13840, GSE144721, and GSE55593) (Fig. 1e). Moreover, human datasets (GSE48452) validated positive correlations between miR-93 expression and clinical metrics, such as BMI and NAS (Extended Data Fig. 1b). In diet-induced obese mouse models, miR-93 expression was significantly elevated in the fatty livers of mice fed a high-fat–high-fructose (HFHFr) diet, Lep^ob/ob mice, and Gubra-Amylin NASH (GAN) diet-fed mice relative to normal chow diet (NCD)-fed WT controls (Fig. 1f-h). Similarly, miR-93 expression was markedly increased in oleic/palmitic acid (OA/PA)-treated hepatocytes (Fig. 1i). Collectively, these data indicate a robust correlation between miR-93 expression and MASLD, highlighting miR-93 as a potential key regulator contributing to MASLD pathogenesis.

miR-93 modulates hepatic steatosis in vivo

To define the role of miR-93 in MASLD, we generated miR-93 knockout (KO) mice via the CRISPR/Cas9 system and compared them to WT mice fed an NCD or HFHFr diet for 16 weeks. In HFHFr diet-fed WT mice, hepatic miR-93 was significantly upregulated (Fig. 2a). Unlike NCD conditions, HFHFr diet-fed miR-93 KO mice showed reduced body weight relative to WT controls (Fig. 2b,c), attributed to lower fat mass and higher lean mass (Fig. 2d,e). Histological analysis revealed decreased liver weight and hepatic steatosis in HFHFr diet-fed miR-93 KO mice (Fig. 2f-h). miR-93 KO mice demonstrated enhanced glucose tolerance, improved insulin sensitivity (Fig. 2i-m), and reduced levels of triglycerides (TG), cholesterol, AST, and ALT (Fig. 2n-q).

Protective effects from diet-induced obesity were similarly observed in a miR-93 KO model of MASLD using the GAN diet. In the GAN diet model of MASLD, miR-93 KO mice showed significant reductions in body and liver weight (Extended Data Fig. 2a-d). Histological analysis, including hematoxylin and eosin (H&E) and Oil Red O staining, confirmed reduced hepatic lipid accumulation and a lower NAS (Extended Data Fig. 2e,f). Metabolic indices were also significantly improved in miR-93 KO mice fed the GAN diet (Extended Data Fig. 2g-j), further supporting the notion that miR-93 deficiency alleviates MASLD progression.

To examine whether miR-93 upregulation drives MASLD, we overexpressed miR-93 via an adenovirus-associated vector (AAV-miR-93) in mice fed either NCD or HFHFr diet. Hepatic miR-93 overexpression in HFHFr diet-fed mice increased body weight and fat mass and decreased lean mass (Extended Data Fig. 3a-d). These mice also showed higher liver weight gain, hepatic steatosis, and NAS (Extended Data Fig. 3e-g) along with impaired glucose tolerance and insulin sensitivity (Extended Data Fig. 3h-l), and elevated TG, cholesterol, AST, and ALT (Extended Data Fig. 3m-p). Thus, miR-93 deficiency mitigates hepatic steatosis and metabolic dysfunction, whereas miR-93 overexpression exacerbates these pathologies.

miR-93 deficiency attenuates hepatic inflammation and fibrosis in MASH

To investigate the role of miR-93 in MASLD to MASH progression, characterized by advanced hepatic steatosis, liver injury, inflammation, and fibrosis, we subjected miR-93 KO mice and WT controls to a 24-week HFHFr diet (MASH diet). This extended feeding regimen stimulates the advanced pathological stages observed in human MASH, including an increased risk of cirrhosis, liver failure, and HCC.²⁰ MASH diet-fed miR-93 KO mice exhibited significantly lower body weight, liver-to-body weight ratio, and fat mass, accompanied by higher lean mass than WT controls (Fig. 3a-e). Histological examination revealed a notable reduction in hepatic steatosis and fibrosis in MASH diet-fed miR-93 KO mice, indicating diminished liver damage (Fig. 3f-h). Correspondingly, MASH diet-fed miR-93 KO mice demonstrated lower levels of hepatic TG, cholesterol, AST, and ALT levels than WT mice on the same diet (Fig. 3i-l). Gene expression analysis further supported these findings, showing significant downregulation of hepatic inflammation-related genes, such as Tnf, Ccl2, Il6, and Il1b, and fibrosis markers, including Acta2, Col1a1, Fn, and Vim, in MASH diet-fed miR-93 KO mice (Fig. 3m). These results highlight miR-93 as a critical regulator of the transition from MASLD to MASH, revealing its role in driving liver inflammation, fibrosis, and steatosis.

miR-93 modulates cholesterol and lipid metabolism in the liver

To further explore the mechanistic role of miR-93 in MASLD progression, we performed RNA sequencing (RNA-seq) on liver samples from HFHFr diet-fed miR-93 KO and WT mice. miR-93 deficiency significantly changed gene expression compared to WT, with 420 genes upregulated and 442 genes downregulated, using an adjusted p-value threshold of < 0.01 and a fold change > 2 (Fig. 4a). GO enrichment analysis of the differentially expressed genes (DEGs) revealed enhanced expression of genes involved in the fatty acid metabolism and FAO in miR-93 KO livers (Fig. 4b), and significant downregulation of steroid metabolism– and cholesterol biosynthesis-related genes (Fig. 4c).

To verify the RNA-seq findings, we examined the expression of key genes involved in FAO and sterol biosynthesis. In HFHFr diet-fed miR-93 KO mice, we observed significant upregulation of Ppara, a master regulator of FAO, along with its target genes, Cpt1a, Acox1, Ppargc1a, and Tfam. In contrast, crucial cholesterol and fatty acid biosynthesis genes, including Srebf1, Srebf2 and their targets Hmgcr, Sqle, Fasn, and Scd1, were markedly downregulated in miR-93 KO livers (Fig. 4d). Similar effects were observed in GAN diet-fed miR-93 KO mice, which showed increased FAO and reduced sterol metabolism in the liver (Extended Data Fig. 2k,l).

To further explore the crucial role of miR-93 in hepatic lipid metabolism and its contribution to MASLD from RNA-Seq analysis, we isolated primary hepatocytes from WT and miR-93 KO mice. Under OA/PA treatment, miR-93 expression was significantly upregulated in WT hepatocytes, whereas miR-93 deficiency substantially reduced intracellular lipid accumulation (Fig. 4e,f). This reduction was accompanied by markedly lower levels of TG and cholesterol in OA/PA-treated miR-93 KO hepatocytes (Fig. 4g,h). Gene expression analysis of miR-93 KO hepatocytes revealed significant upregulation of lipid catabolism-related genes (Fig. 4i). In contrast, expression of Srebf1/2 and their downstream targets significantly decreased in miR-93 KO hepatocytes (Fig. 4j). In addition, miR-93 deficiency enhanced both basal and maximal respiratory capacities in hepatocytes under basal and OA/PA-treated conditions (Fig. 4k,l). Furthermore, ATP levels and mitochondrial DNA copy number increased in miR-93 KO hepatocytes, indicating improved mitochondrial function (Fig. 4m,n).

We also observed the relationship between miR-93 overexpression and lipid metabolism in hepatic steatosis by transducing primary hepatocytes with AAV-miR-93. Although miR-93 overexpression alone did not substantially increase lipid accumulation, it significantly elevated TG and cholesterol levels under OA/PA treatment (Extended Data Fig. 4a-d). Moreover, miR-93 overexpression under OA/PA conditions reduced lipid catabolism gene expression, while upregulating DNL and cholesterol biosynthesis genes (Extended Data Fig. 4e,f). Mitochondrial function and DNA copy number decreased following miR-93 overexpression and OA/PA treatment (Extended Data Fig. 4g-j). Collectively, these findings indicate that miR-93 deficiency mitigates hepatic lipid accumulation by promoting FAO and mitochondrial function while reducing TG and cholesterol biosynthesis, providing mechanistic insights into its role in MASLD progression.

SIRT1 is a direct target of miR-93

To identify the molecular target of miR-93 in MASLD progression, bioinformatic analysis predicted six potential target genes. Among these, SIRT1 emerged as the most compelling candidate (Fig. 5a). Consistent with these predictions, miR-93 overexpression significantly reduced Sirt1 expression, whereas miR-93 KO markedly increased Sirt1 levels in hepatocytes (Extended Data Fig. 5a,b).

Further analysis identified a conserved miR-93 binding site within the 3’ untranslated region (UTR) of SIRT1 mRNA (Fig. 5b). To confirm miR-93 directly targets SIRT1, we performed luciferase reporter assays in HepG2 cells transfected with WT or mutant 3’ UTR SIRT1 constructs. miR-93 overexpression significantly suppressed luciferase activity in WT, but not mutant, 3’UTR constructs (Fig. 5c). Similarly, AAV-miR-93 administration in hepatocytes reduced Sirt1 mRNA and protein levels (Fig. 5d,e), whereas miR-93 suppression elevated Sirt1 expression (Fig. 5f,g).

In MASLD mouse models, miR-93 overexpression greatly reduced Sirt1, whereas miR-93 KO significantly upregulated Sirt1 (Fig. 5j,k and Extended Data Fig. 6a,b). Given that cellular NAD⁺ levels regulate Sirt1 gene expression and protein activity^{21, 22}, miR-93 KO mice exhibited increased NAD⁺ concentrations and SIRT1 activity in hepatocytes and liver tissues (Fig. 5h,i,l,m). In contrast, miR-93 overexpression reduced both NAD⁺ levels and SIRT1 activity (Extended Data Fig. 6c,d). In liver samples from patients with MASLD, SIRT1 and FAO genes were downregulated, whereas cholesterol and lipid synthesis genes were upregulated compared to healthy controls (Fig. 5n,o). Correlation analysis further revealed a negative association between miR-93 and SIRT1, and SIRT1-related FAO target genes. Conversely, miR-93 positively correlated with cholesterol and lipid synthesis genes (Fig. 5p). Together, these findings establish SIRT1 as a direct target of miR-93 and demonstrate that miR-93 modulates the balance between lipid synthesis and oxidation, contributing to MASLD progression.

miR-93/SIRT1 axis regulates hepatic steatosis via the LKB1-AMPK signaling pathway

Previous studies demonstrated that SIRT1 promotes FAO and inhibits DNL and cholesterol biosynthesis in the liver.^{23, 24} This regulation is mediated by the LKB1–AMPK signaling pathway, a key player in MASLD pathogenesis.^{18, 19} Based on our findings, we hypothesized the metabolic improvements observed in miR-93-deficient mice were mediated by the SIRT1/LKB1/AMPK axis. miR-93 KO mice on both HFHFr diet and GAN diet exhibited significant LKB1 and AMPK activation, and notable reduction in overall and active cleaved SREBF1/2 (Fig. 6a, Extended Data Fig. 7a). Similar effects were observed in hepatocytes of miR-93 KO mice (Fig. 6b). In contrast, miR-93 overexpression resulted in decreased phosphorylation of LKB1 and AMPK in both the livers of HFHFr diet-fed mice and hepatocytes (Extended Data Fig. 7b,c). Under NCD conditions, miR-93 KO or overexpression moderately changed LKB1 and AMPK phosphorylation (Extended Data Fig. 7d,e).

We further elucidated the role of the miR-93/SIRT1 axis in regulating hepatic lipid metabolism. In hepatocytes from miR-93 KO mice, lentiviral Sirt1 knockdown (shSirt1) significantly attenuated elevated SIRT1 levels and LKB1-AMPK phosphorylation (Fig. 6c). Moreover, miR-93 deficiency enhanced the expression of Sirt1 and fatty acid oxidation-related genes, while inhibiting cholesterol biosynthesis and DNL. Sirt1 knockdown abolished these effects (Fig. 6d-f). Additionally, the metabolic benefits observed in miR-93 KO mice substantially diminished following Ampk knockdown using siRNA (siAmpk) (Fig. 6g-i). Together, these results strongly suggest that the miR-93/SIRT1 axis regulates hepatic lipid and cholesterol metabolism by modulating the LKB1-AMPK signaling pathway, thereby influencing MASLD progression.

miR-93 promotes hepatic steatosis through SIRT1 modulation

Next, we performed in vivo lentiviral knockdown of Sirt1 using Sirt1 shRNA (shSirt1) or Control shRNA (shControl) in HFHFr diet-fed WT and miR-93 KO mice. In WT mice, Sirt1 knockdown minimally affected body weight, fat mass, and lean mass (Fig. 7a-d). As expected, miR-93 deficiency significantly reduced hepatic steatosis by enhancing the LKB1-AMPK signaling pathway compared to WT control. However, Sirt1 knockdown in miR-93 KO mice significantly increased liver-to-body weight ratio compared to untreated miR-93 KO mice (Fig. 7e). Furthermore, LKB1 and AMPK phosphorylation significantly decreased in miR-93 KO mice with Sirt1 knockdown, suggesting SIRT1 is crucial for LKB1-AMPK activation (Fig. 7f,g). Histological analysis showed increased hepatic lipid accumulation with elevated metabolic parameters in miR-93 KO mice following Sirt1 knockdown (Fig. 7h-m). Additionally, Sirt1 knockdown exacerbated hepatic steatosis in both WT and miR-93 KO mice by decreasing FAO and increasing DNL and cholesterol biosynthesis (Fig. 7n,o). These findings indicate that SIRT1 is a pivotal regulator in hepatic lipid metabolism, and its modulation by miR-93 plays a central role in the pathogenesis of MASLD.

Niacin alleviates MASLD via modulation of the miR-93/SIRT1 axis

Despite progress in gene therapies, effective FDA-approved treatments for MASLD remain lacking due to limited efficacy and adverse effects.^{12, 13} Given the pivotal role of miR-93 in MASLD progression, we explored its potential as a therapeutic target. Using a high-throughput screening system, we aimed to identify FDA-approved drugs that modulate miR-93 expression. We developed a miR-93 inhibitor screening system with the miR-93 promoter driving a luciferase reporter (Fig. 8a). Among a library of 150 FDA-approved drugs, screening revealed niacin most effectively inhibited miR-93 expression (Fig. 8b). Dose-response studies confirmed niacin inhibition of the miR-93 promoter in primary hepatocytes (Fig. 8c).

Niacin, also known as vitamin B3 or nicotinic acid, is essential for regulating lipid and cholesterol levels and participating in NAD⁺ production, which modulates SIRT1 activity.²⁵ Thus, we administered AAV-Control or AAV-miR-93 to WT mice on an HFHFr diet for eleven weeks, followed by niacin supplementation for an additional five weeks (Fig. 8d). Interestingly, niacin decreased miR-93 level in AAV-Control mice, but showed no effect in AAV-miR-93 mice (Fig. 8e). Furthermore, Niacin did not affect body weight in either group (Fig. 8f); however, in AAV-Control mice, but not AAV-miR-93 mice, niacin significantly reduced the liver-to-body weight ratio (Fig. 8g). Histological analysis and metabolic assessments revealed substantial improvements in hepatic TG, cholesterol, and serum AST and ALT levels in AAV-Control mice receiving niacin. Niacin benefits were limited in AAV-miR-93 mice (Fig. 8h-m). Furthermore, niacin supplementation in AAV-Control mice increased Sirt1 mRNA and protein levels, enhanced LKB1-AMPK signaling, and improved FAO with reduced lipid and cholesterol synthesis. Conversely, niacin did not affect lipid metabolism genes in AAV-miR-93-injected mice (Fig. 8n-q). These results suggest niacin effectively improves MASLD by modulating SIRT1 through miR-93 regulation, though its efficacy diminishes with elevated miR-93 levels (Fig. 8r).

The rising prevalence of MASLD underscores its significance as the most common liver disease worldwide.¹ MASLD is associated with obesity and various metabolic disorders, such as T2DM and CVD, highlighting the critical need to understand the molecular mechanisms underlying MASLD progression.^4–6 Although liver regulation of glucose and lipid homeostasis are well-established, the specific pathways driving MASLD progression remain incompletely understood.

This study identified miR-93 as a pivotal regulator in MASLD progression through a comprehensive analysis of miRNA candidates. Our results are consistent with recent miRNA profiling studies reporting elevated miR-93 levels in patients with MASLD.²⁶ We elucidated a novel mechanism wherein miR-93 modulates essential metabolic pathways, including FAO, cholesterol biosynthesis, TG synthesis, and mitochondrial ATP production. miR-93 downregulation of SIRT1 is essential for these effects. SIRT1 is crucial for energy homeostasis, influencing FAO, cholesterol biosynthesis, and DNL.^{27, 28} Our data suggest that miR-93-mediated downregulation of SIRT1 disrupts lipid metabolism and mitochondrial function, thereby driving MASLD progression.

Previous research has implicated several miRNAs, such as miR-34a, miR-122, and miR-506, in SIRT1 regulation during MASLD. Specifically, miR-34a increases reactive oxygen species production, leading to SIRT1 downregulation and reduced FAO.²⁹ Similarly, miR-122 and miR-506 are linked to SIRT1 regulation and AMPK signaling, affecting DNL and exacerbating MASLD.^{30, 31} Our study extends these findings by identifying miR-93 as a novel SIRT1 regulator with a dual role in lipid and cholesterol metabolism. The dual regulatory function underscores the critical role miR-93 plays in MASLD pathogenesis and suggests the therapeutic potential of targeting the miR-93/SIRT1 axis.

Moreover, our findings suggest the miR-93/SIRT1 axis is crucial for lipid metabolism and plays a role in other metabolic disorders. Given the involvement of these pathways in broader metabolic processes, the interactions between miR-93 and SIRT1 could have implications for the transition from MASLD to MASH. Our miR-93 KO model demonstrated significant reductions in hepatic inflammation and fibrosis via downregulation of key proinflammatory cytokines and fibrotic markers. The protective role of SIRT1 in these processes is well documented, with anti-inflammatory and anti-fibrotic effects largely mediated through AMPK activation and suppression of TGF-β signaling pathways.^{18, 32, 33} The observed improvements in miR-93 KO mice further underscore the potential of targeting the miR-93/SIRT1 axis to prevent fibrosis and slow MASLD progression.

The lack of FDA-approved pharmacological treatments for MASLD necessitates novel therapeutic strategies. Our high-throughput screening identified niacin (vitamin B3) as a potential therapeutic agent capable of modulating miR-93 expression. Known for its broad metabolic effects including reduced hepatic TG synthesis, cholesterol biosynthesis, inflammation, and enhanced FAO and insulin sensitivity^{34, 35}, niacin also affects SIRT1 through miR-93 regulation. Our data demonstrate that niacin improves MASLD by enhancing the miR-93/SIRT1 axis and influencing the LKB1/AMPK signaling pathway. However, the therapeutic efficacy was attenuated in models with miR-93 overexpression, indicating resistance to the beneficial effects of niacin with elevated miR-93 levels. This finding highlights the importance of contextual therapeutic strategies for MASLD treatment, in which miR-93 expression levels may dictate treatment response.

Given the central role of miR-93 in regulating lipid metabolism and fibrosis, our findings suggest that miR-93-targeted therapies could provide a tailored approach to MASLD management. Although miRNA-based therapies face significant challenges, such as delivery and specificity, our finding underscores the promise of targeting miR-93 to restore SIRT1 function and mitigate metabolic dysfunctions in MASLD. Moreover, the ability of niacin to modulate miR-93 expression points to the potential for combination therapies that target multiple facets of the disease, including metabolic disorders, inflammation, and fibrosis. Future clinical trials are warranted to explore the efficacy of miR-93-targeted therapies, either alone or in combination with other metabolic modulators, in treating MASLD and its complications.

Human patients

Liver specimens from 11 patients with MASLD were obtained from the division of Hepatology, Department of Internal Medicine, University of Ulsan College of Medicine, Ulsan University Hospital (UUH 2023-04-039-004, Ulsan, Republic of Korea). Additionally, 7 normal healthy controls of liver samples were obtained from the Bio Resource Center (BRC) of Asan Medical center (Seoul, Republic of Korea).

The liver biopsies were performed by an experienced diagnostic radiologist, and histological samples from 11 enrolled patients were evaluated by a pathologist with over 10 years of experience. Clinical and laboratory parameters were also assessed. All hepatic histology slides were of sufficient quality to allow for accurate grading and staging of the liver biopsy features. Only liver biopsies that the pathologist deemed of adequate size, containing a sufficient number of portal tracts for reliable grading and staging, were included.

The grade of various histological features was assessed using Hematoxylin and Eosin (H&E)-stained tissue, while fibrosis staging was evaluated on Masson’s Trichrome-stained samples. A diagnosis of steatotic liver disease required a threshold of 5% hepatocytes exhibiting steatosis. All participants had greater than 5% steatosis and at least grade 1 lobular inflammation, but were categorized according to the steatohepatitis scoring system. The non-alcoholic fatty liver disease (NAFLD) activity score (NAS) was calculated as the unweighted sum of the scores for steatosis (0–3), lobular inflammation (0–3), and hepatocellular ballooning (0–2), yielding a total score ranging from 0 to 8.³⁶ Based on prior studies, participants were grouped into three categories: NAS ≤ 2, NAS 3–4, and NAS ≥ 5.^{37, 38} Additionally, we included results from a recently recommended scoring system by the Korean Society of Pathology for diagnosing metabolic-associated steatohepatitis (MASH)³⁹, which is the newly suggested model was calculated the following equation: 2 x sum of major score (ballooning change + fibrosis score) + minor. According to these systems, the pathologist classified the diagnosis into three categories: not-MASH (0–3 points), borderline/suspicious MASH (4–5 points), or definitive MASH (6–11 points). Fibrosis was presented according to the METAVIR system and classified into distinct stages based on histopathological findings. Stage 0 indicates the absence of fibrosis. Stage 1 is subdivided into three categories: 1A, representing mild perisinusoidal fibrosis confined to zone 3; 1B, denoting moderate perisinusoidal fibrosis in zone 3; and 1C, indicating fibrosis localized to the portal or periportal regions. Stage 2 reflects fibrosis that involves both perisinusoidal and portal/periportal areas. Stage 3 is characterized by bridging fibrosis, while stage 4 corresponds to established cirrhosis. The extended data table 1 provides detailed descriptions of the newly proposed scoring system and the results of histological parameters relevant to diagnosing MASH. Additionally, the FibroScan® results obtained prior to the biopsy for the patients included in the study were also presented (Extended data table 1).

Cell culture

Primary hepatocytes were isolated as previously described.⁴⁰ Male mice were anesthetized with isoflurane, and the livers were perfused with solution A (142 mM NaCl, 67 mM KCl, 10 mM HEPES, and 2.5 mM EGTA) and subsequently with solution B (66.7 mM NaCl, 6.7 mM KCl, 50 mM HEPES, 4.8 mM CaCl₂·2H₂O and 0.02% Type IV collagenase (Sigma-Aldrich, St. Louis, MO)) until the liver was thoroughly digested. The liver was dissected in M199/EBSS medium, then were filtered through a 70 µm cell strainer. After centrifugation at 50 x g for 7 min, the pellets were resuspended in the M199/EBSS medium. Mouse primary hepatocytes were separated from 27% percoll solution at 200 x g for 2 min, and the cells were washed with M199/EBSS medium. Primary hepatocytes were cultured in M199/EBSS supplemented with 10% FBS (Gibco, BRL, Grand Island, NY), 10nM dexamethasone with 1% penicillin/streptomycin (Thermo Fisher Scientific, Waltham, MA).

Primary hepatocytes were transfected with AMPK siRNA (siAMPK) using Lipofectamine™ RNAiMAX transfection reagent (Thermo Fisher Scientific, Waltham, MA) according to the manufacturer’s instructions. For intracellular lipid accumulation, cells were cultured in a medium with the addition of 1 mmol/L sodium oleic acid/palmitic acid mixture for 24 hours and then cells were harvested for further analysis. Images of cells stained with Oil Red O were performed with EVOS FL (Thermo Fisher Scientific, Waltham, MA).

Immortalized human liver cell line, HepG2 cells, was purchased from American Type Culture Collection (ATCC, Manassas, VA) and maintained in Dulbecco’s Modified Eagle’s Medium (DMEM) with 10% FBS and 1% penicillin/streptomycin.

Mice

All animal experiments were performed according to procedures approved by the Ulsan National Institute of Science and Technology’s Institutional Animal Care and Use Committee (UNISTIACUC-23-35). Mice were maintained in a specific pathogen-free animal facility under a 12-h light/dark cycle at a temperature of 21°C and allowed free access to water and food. Generation of CRISPR/Cas9-mediated miR-93 knockout mouse for miR-93 targeting gRNA design compared them with wild-type (WT) C57BL/6J mice (DBL, Chungbuk, Republic of Korea) were fed a HFD (60% kcal fat, D12492, Research Diets Inc., New Brunswick, NJ) with glucose (G8270, Thermo Fisher Scientific, Waltham, MA) and fructose (F3510, Thermo Fisher Scientific, Waltham, MA) for 16 weeks or 24 weeks and a GAN (40% kcal fat with 20% fructose and 2% cholesterol, D09100310, Research Diets Inc., New Brunswick, NJ, USA) for 16 weeks, Niacin (200 mg/kg, 72039, Thermo Fisher Scientific, Waltham, MA) was supplemented with water for 5 weeks before mice were sacrificed.^41–43

Analysis of the publicly available hepatic transcriptome with human and mouse

Human MASLD specimens (GSE48452 and GSE135251) and murine models of the disease (GSE13840, GSE144721, and GSE55593) from the NCBI GEO database. The data were normalized using the trimmed mean of M-values (TMM) normalization method in EdgeR(https://bioconductor.org/packages/release/bioc/html/edgeR.html).

Correlation plots were analyzed between miR-93 expression and clinical parameters such as BMI and NAS score using GSE48452.

RNA sequencing and bioinformatics analysis

RNA sequencing was performed from the livers of HFHFr diet-fed wild-type and miR-93 KO mice. For preparation of RNA library was previously described.⁴⁰ Total RNA was extracted from the tissues using the RNeasy mini kit (Qiagen, Hildan, Germany) according to the manufacturer’s guidelines. Library prep and RNA sequencing were performed by Novogene (Biopolis, Singapore). For RNA sequencing, 1 µg of total RNA was determined as input material for the RNA library preparation using NEBNext® UltraTM RNA Library Prep Kit for Illumina® (NEB, Ipswich, MA) following manufacturer’s instructions. PCR products were further purified with AMPure XP system (Beckman Coulter Life Sciences, Indianapolis, IN) and library quality was examined on the Agilent Bioanalyzer 2100 (Agilent Technologies, Inc., Santa Clara, CA). The clustering of the index-coded samples was assessed on a cBot Cluster Generation System using PE Cluster Kit cBot-HS (Illumina, San Diego, CA), followed by the manufacturer’s protocols. Sequencing was performed on an Illumina platform (NovaSeq 6000 PE150) and paired-end reads were generated. The quality of raw RNA sequencing reads in FASTQ format was assessed using FastQC (v.0.11.9) to assess the distribution of Phred quality scores and the average GC content across the reads. Subsequently, low-quality bases and adapter sequences were eliminated from the reads using TrimGalore (v.0.6.6). The cleaned reads were then aligned to the reference mouse genome (GRCm39) employing the STAR aligner (v.2.7.9a). To quantify gene expression levels, read count and transcripts per million (TPM) were calculated utilizing RESM (v.1.3.3), which assembled the aligned reads into genes and estimated their abundance.

Differential gene expression analysis and functional annotation

Differential gene expression between groups (miRNA-93 knockout vs. wild-type) was determined through Wald tests implemented in the DESeq2 R package (v4.3.1). Resulting P-values were adjusted for false discovery rate (FDR) control using the Benjamini-Hochberg method. Genes with FDR adjusted P-value < 0.01 and an absolute log2 fold change (|log2FC|) exceeding 1 were designated as differentially expressed genes (DEGs).

To explore the molecular functions associated with these DEGs, a hypergeometric test was conducted with the Gene Ontology (GO) database, via the R package clusterProfiler (v. 4.10.1). The significance of enrichment was determined using q-value (FDR-adjusted P-value). The hierarchy of enriched GO terms was visualized as a directed acyclic graph using function in R function goplot. Additionally, gene set enrichment analysis (GSEA) was performed to identify pathways significantly overrepresented at the top or bottom of ranked gene lists derived from the differential gene expression analysis between groups. This GSEA analysis was executed using the R package fgsea (v. 1.28.0).

miRNA regulatory network analysis

We obtained 2735 downregulated genes in MASLD compared to the normal condition from published RNA-seq data (adjusted p-value < 0.05, fold change < 1).⁴⁴ Among them, 53 genes were annotated with “REGULATION OF LIPID METABOLIC PROCESS” in Gene Ontology (GO) (MsigDB v2023.2).^45–47 For these genes, we performed a target enrichment test to identify upstream miRNAs. We assumed miRNAs whose targets are enriched in those downregulated genes are involved in the regulation of lipid metabolic process in MASLD. miRNA families and their target genes were obtained from the TargetScan database (V80, human).⁴⁸ MicroRNA targets not observed in the background set (c2 or c5 categories in MSigDB) were removed a priori. Hypergeometric tests were used to identify miRNAs whose targets were enriched in the downregulated GO genes compared to the background. Group 1 (842 genes): Gene Ontology (GO) REGULATION OF LIPID METABOLIC PROCESS pathway genes; Group 2 (1,314 genes): microRNA target genes; Group 3 (53 genes): downregulated genes in patients with MASLD annotated with the GO term REGULATION OF LIPID METABOLIC PROCESS.

Immunoblotting

Supernatants containing protein contents were determined by Bradford Protein Assay (Bio-Rad Laboratories, Hercules, CA). Proteins were immunoblotted with anti-SIRT1 (9475, Cell Signaling Technology, Danvers, MA), anti-phospho-LKB1 (3482, Cell Signaling Technology, Danvers, MA), anti-LKB1 (3047, Cell Signaling Technology, Danvers, MA), anti-phospho-AMPK (2535, Cell Signaling Technology, Danvers, MA), anti-AMPK (2532, Cell Signaling Technology, Danvers, MA), anti-SREBP1 (sc13551, Santa Cruz biotechnology, Dallas, TX), anti-SREBP2 (sc13552, Santa Cruz biotechnology, Dallas, TX) and anti-HSP90 (4877, Cell signaling Technology, Danvers, MA).

Quantitative PCR

Total RNA was isolated using a TRIzol reagent (Thermo Fisher Scientific, Waltham, MA). RNA reverse transcription was performed using an ABI Reverse Transcription Kit (Thermo Fisher Scientific, Waltham, MA). Quantitative PCR was performed with a CFX Duet Real-Time PCR system (Bio-Rad, Hercules, CA) following the manufacturer’s instructions. Relative mRNA expression levels of each gene were normalized to the expression level of the TATA-binding protein TBP. The relative expression levels of miRNA were examined by reverse transcribing with TaqMan miRNA reverse transcription kit (Thermo Fisher Scientific, Waltham, MA), normalized with the universal primer (U6) expression level following the manufacturer’s protocol. The mtDNA copy number was evaluated based on the ratio of mtDNA to nuclear DNA by quantitative PCR. The mtDNA was quantified based on the mitochondrial gene Nd1. The nuclear DNA was quantified based on the nuclear DNA gene Actb. The sequences of primers used were as following:

	Sequences of the primers for qPCR
Gene	Forward primer	Reverse primer
SIRT1	CCCTCAAAGTAAGACCAGTAGC	CACAGTCTCCAAGAAGCTCTAC
PPARA	GCTATCATTACGGAGTCCACG	TCGCACTTGTCATACACCAG
CPT1A	AGGCGACATCAATCCGAAC	AAAGGCTACGAATGGGAAGG
ACOX1	CCACGTATGACCCTGAAACC	TCCATAGCATTTCCCCTTAGTG
SREBF2	CCTGGGAGACATCGACGAGAT	TGAATGACCGTTGCACTGAAG
HMGCR	TGATTGACCTTTCCAGAGCAAG	CTAAAATTGCCATTCCACGAGC
SREBF1	CAACACAGCAACCAGAAACTC	CTCCACCTCAGTCTTCACG
TBP	CCACTCACAGACTCTCACAAC	CTGCGGTACAATCCCAGAACT
Sirt1	TGATTGGCACCGATCCTCG	CCACAGCGTCATATCATCCAG
Ppara	TCAGGGTACCACTACGGAGT	CTTGGCATTCTTCCAAAGCG
Cpt1a	AGTTCCATGACCCATCTCTGTC	TTCTTCTTCCAGAGTGCAGC
Acox1	TAACTTCCTCACTCGAAGCCA	AGTTCCATGACCCATCTCTGTC
Ppargc1a	TATGGAGTGACATAGAGTGTGCT	GTCGCTACACCACTTCAATCC
Tfam	AACACCCAGATGCAAAACTTTCA	GACTTGGAGTTAGCTGCTCTTT
Srebf2	GCAGCAACGGGACCATTCT	CCCCATGACTAAGTCCTTCAACT
Hmgcr	TGTTCACCGGCAACAACAAGA	CCGCGTTATCGTCAGGATGA
Sqle	AGTTCGCTGCCTTCTCGGATA	GCTCCTGTTAATGTCGTTTCTGA
Srebf1	GGAGCCATGGATTGCACATT	CTTCCAGAGAGGAGGCCAG
Fasn	GGAGGTGGTGATAGCCGGTAT	TGGGTAATCCATAGAGCCCAG
Scd1	TTCTTGCGATACACTCTGGTGC	CGGGATTGAATGTTCTTGTCGT
Arid4a	GATGAGCCTGCCTACCTGAC	CACCAACTGTGTTGTGTTATCCT
Cyb5b	GAGCCCTCCGTCACCTACTA	AGCTTTCAGTTGCATCAGCAC
Frs2	AGCTGTCCAGATAAAGACACTGT	ATTTTACCGAGTCCCGTTTCC
Plcb1	GCCCCTGGAGATTCTGGAGT	GGGAGACTTGAGGTTCACCTTT
Nd1	CAGCCTGACCCATAGCCATA	ATTCTCCTTCTGTCAGGTCGAA
Tnf	CCCTCACACTCAGATCATCTTCT	GCTACGACGTGGGCTACAG
Ccl2	TTAAAAACCTGGATCGGAACCAA	GTTCACCGTAAGCCCAATTT
Il6	TAGTCCTTCCTACCCCAATTTCC	TTGGTCCTTAGCCACTCCTTC
Il1b	GCACTACAGGCTCCGAGATGAAC	TTGTCGTTGCTTGGTTCTCCTTGT
Acta2	GTGAAGAGGAAGACAGCACAG	GCCCATTCCAACCATTACTCC
Col1a1	CATAAAGGGTCATCGTGGCT	TTGAGTCCGTCTTTGCCAG
Fn	CTTTGGCAGTGGTCATTTCAG	ATTCTCCCTTTCCATTCCCG
Vim	CGTCCACACGCACCTACAG	GGGGGATGAGGAATAGAGGCT
Tbp	ACCCTTCACCAATGACTCCTATG	TGACTGCAGCAAATCGCTTGG
Actb	GGCTGTATTCCCCTCCATCG	CCAGTTGGTAACAATGCCATGT
miR-93	AAGTGCTGTTCGGCAGTAGTG	TTCACGACGAGTCGTCATCAC
miR-15a	GCAGCACATAATGGTTTG	GAACATGTCTGCGTATCTC
miR-15b	CGCGCGTAGCAGCACATCATGG	ATCCAGTGCAGGGTCCGAGG
miR-16	TGGGGTAGCAGCACGTAAA	CTCAACTGGTGTCGTGGAGTC
miR-195	TAGCAGCACAGAAATAT	GTGCAGGGTCCGAGGT
miR-424	GGCAGCAGCAATTCATG	CAGTGCGTGTCGTGGAGT
miR-497	GTGCAGGGTCCGAGGT	TAGCCTGCAGCACACTGTGGT
miR-106a	GATGCTCAAAAAGTGCTTACAGTGCA	TATGGTTGTTCTGCTCTCTGTCTC
miR-106b	CAAGTACCCACAGTGCGGT	CTCGCTTCGGCAGCACA
miR-17	TCTAGATCCCGAGGACTG	ATCGTGACCTGAACC
miR-20a	TGCGCTAAAGTGCTTATAGTGC	CCAGTGCAGGGTCCGAGGTATT
miR-20b	GCAAAGTGCTCATAGTGCAGGTAG	TCGCACTTGTCATACACCAG
miR-526b	GCGCTCTTGAGGGAAGCACT	TACGTTCCATAGTCTACCA
miR-519d	AGTGCCTCCCTTTAGAG	GAACATGTCTGCGTATCTC
U6	CTCGCTTCGGCAGCACA	AACGCTTCACGAATTTGCGT

Cellular oxygen consumption rate (OCR)

OCR of primary hepatocytes was analyzed by a Seahorse XF24 extracellular flux analyzer (Seahorse Bioscience, North Billerica, MA) following the manufacturer’s instructions. Reagents were injected into the assay plate at 2 µM Oligomycin, 1 µM FCCP, and 0.5 µM Rotenone/Antimycin A, respectively. The results were further normalized to the protein concentration of each 24-well plate.

Production of Adeno-associated virus and lentivirus

miR-93 was cloned into the pOTTC385-pAAV CMV-IE IRES EGFP vector (Addgene plasmid # 102936) and co-transfected with pAAV-DJ vector and pAAV-Helper vector into HEK 293T cells to generate recombinant adeno-associated virus according to the manufacturer’s protocol (Cell Biolabs, SanDiego, CA). The AAVs were purified with AAVpro® Purification Kit (Takara Bio, Shiga, Japan). When mice fed the HFHFr diet were started, purified AAV-miR-93 or AAV-Control (1 X 10¹⁰ PFU) was injected into mice via tail-vein.

Lentivirus-based Sirt1 shRNA clones used for making the viral transduction particles were purchased from Sigma-Aldrich. pLKO.1-Puro vector targeting mouse Sirt1 or control was used. HEK 293T cells were co-transfected with a complex containing a three-plasmid system (pLKO.1 shRNA, pCMV-VSVG, and pCMV-Δ8.9) at the ratio of 4:2:3 using lipofectamine. The supernatant was collected starting from 72 hours post-transfection. The virus particles were concentrated by ultracentrifugation at 25,000 rpm for 1 hour 30 minutes with a Beckman ultracentrifuge using the SW28 rotor, resuspended in PBS. The lentivirus particles were purified with Lenti-X™ Maxi Purification Kit (Clontech, Mountain View, CA). When mice fed with the HFHFr diet were started, purified shSirt1 or shControl lentivirus (1 X 10¹¹ PFU) was infected into mice via tail-vein.

Biochemical analysis

The body weight of every mouse was measured weekly. HFHFr diet-fed mice were fasted overnight (18 hours) before intraperitoneal injection of D-glucose (2 g/kg body weight) for the glucose tolerance test. For the insulin tolerance test, mice were fasted for 4 hours before intraperitoneal injection of insulin (1 U/kg body weight). Glucose levels were assessed with tail vein blood at the indicated time points after injection using a glucometer.

To analyze metabolic parameters, TG (10010303, Cayman Chemical, Ann Arbor, MI), cholesterol (BM-CHO-100, BioMax, Republic of Korea), ALT (700260, Cayman Chemical, Ann Arbor, MI), AST (701640, Cayman Chemical, Ann Arbor, MI) and insulin ELISA kit (90080, Crystal Chem, Elk Grove Village, IL) were performed. The body composition of the mice was measured using a quantitative nuclear resonance system EchoMRI100V (Echo Medical Systems, Houston, TX).

Histological analysis

Mouse liver tissues were harvested and subsequently fixed with 4% formalin. Liver sections were stained with H&E staining and Oil Red O staining to evaluate lipid accumulation. Mayer’s hematoxylin was used as a counterstain for every slide. Liver fibrosis was further examined by Sirius red staining. Images were obtained with CELENA® X High Content Imaging System (Logos Biosystems, Gyeonggi-do, Republic of Korea).

Human liver tissues of patients with MASLD were deparaffinized in xylene, followed by serial dilutions of ethanol (100–90%), and incubated in DNase/RNase-free distilled water. Slides were incubated in 3% H₂O₂ for 30 min at room temperature, and washed with nuclease-free distilled water. Digested with proteinase K (P2308, Sigma-Aldrich, St. Louis, MO) at 37°C for 20 min, washed with nuclease-free distilled water. Slides were then incubated with 10 pmol of locked nucleic acid (LNA)-modified miR-93 probe (Macrogen, Seoul, Republic of Korea), which was labeled with digoxigenin (DIG) at overnight. After hybridization, the slides were washed in saline sodium citrate (SSC), 0.2% Tween-20 at 45°C, followed by incubates in SSC and nuclease-free water at room temperature. Slides were then incubated with blocking solution, subsequently treated with a horseradish peroxidase (HRP)-conjugated anti-DIG antibody. The signal of hybridized miR-93 probe was detected by 3,3’-diaminobenzidene (DAB) staining. Images were obtained with an Olympus BX53 microscope and DP26 camera.

Measurement of SIRT1 activity and NAD⁺ concentration

Primary hepatocytes and liver tissues were lysed with the buffer containing 10 mM Tris HCl (pH 7.5), 10 mM NaCl, 15 mM MgCl₂, 250 mM Sucrose, 0.5% NP-40, 0.1 mM EGTA with protease and phosphatase inhibitors. Resuspended the isolated nuclei with the extraction buffer (50 mM HEPES KOH (pH 7.5), 420 mM NaCl, 0.5 mM EDTA, 0.1 mM EGTA, 10% glycerol) and then, sonicate the samples. The activity of SIRT1 was assessed by using the SIRT1 activity assay kit (ab156065, Abcam, Cambridge, UK) as followed by the manufacturer’s guidelines.

NAD⁺ concentration was measured in cells and liver tissues using NAD⁺ and NADH assay kit (ab65348, Abcam, Cambridge, UK) as instructed by the manufacturer.

Statistical analysis

All data are presented as the mean ± SEM. Statistical analyses were performed using GraphPad Prism 9.3.1 (GraphPad Software, La Jolla, CA, USA). Generally, a two-tailed Student’s t-test was used to evaluate statistical differences between the means of the two groups. A t-test with Welch’s correction was used to compare the two groups when appropriate. One-way or two-way ANOVA followed by Tukey’s test for multiple group comparisons. The threshold for statistical significance was indicated at *, for p < 0.05; **, for p < 0.01; and ***, for p < 0.001.

Data availability

The RNA sequencing data have been deposited in the National Center for Biotechnology Information Gene Expression Omnibus (GEO) under accession codes GSE277560.

Ethics declarations

Competing interests

The authors declare no competing interests.

Acknowledgments

This research was funded by the National Research Foundation of Korea grant funded by the Korean government (2021R1I1A204146314, 2018R1A5A1024340, RS-2023-00249887, 2023R1A2C1007018), The Korea Drug Development Fund funded by Ministry of Science and ICT, Ministry of Trade, Industry, and Energy, and Ministry of Health and Welfare (RS-2022-DD123898) and the Korea Research Institute of Bioscience & Biotechnology (KRIBB) (KGM539241410990).

Authors information

Authors and Affiliations

Department of Biological Sciences, Ulsan National Institute of Science and Technology (UNIST), Ulsan 44919, Republic of Korea

Yo Han Lee, Kieun Park, Bukyung Baik, Kimyeong Kim, Keon Woo Khim, Haneal Ji, Ji Young Lee, Kwangho Kim, Ji Won Kim, Haejin Yoon, Dougu Nam, Jang Hyun Choi

College of Pharmacy and Research Institute for Drug Development, Pusan National University (PNU), Busan 46241, Republic of Korea

Jinyoung Lee, Yuseong Kwon, Seonghwan Hwang, Haeseung Lee, Hwayoung Yun

Division of Hepatology, Department of Internal Medicine, University of Ulsan College of Medicine, Ulsan University Hospital (UUH), Ulsan 44033, Republic of Korea

Joonho Jeong, Neung Hwa Park

Department of Biomedical Science and Engineering, Gwangju Institute of Science and Technology (GIST), Gwangju 61005, Republic of Korea

Department of Molecular Cell Biology, Sungkyunkwan University School of Medicine (SKKU), Suwon 16419, Republic of Korea

Tam Dao, Dongryeol Ryu

Department of Pathology, University of Ulsan College of Medicine, Ulsan University Hospital (UUH), Ulsan 44033, Republic of Korea

Misung Kim

Department of Radiology, University of Ulsan College of Medicine, Ulsan University Hospital (UUH), Ulsan 44033, Republic of Korea

Tae Young Lee

Aging Convergence Research Center, Korea Research Institute of Bioscience and Biotechnology (KRIBB), Daejeon 34141, Republic of Korea

Yong Ryoul Yang

Metabolic Regulation Research Center, Korea Research Institute of Bioscience and Biotechnology (KRIBB), Daejeon 34141, Republic of Korea

Won Kon Kim

Contributions

Y.H.L., J.L., J.J., K.P., and J.H.C conceived and designed the experiments. N.H.P. and H.Y., and J.H.C conceived and supervised the study. Y.H.L., J.L., J.J., K.P., K.K., K.W.K., H.J., J.Y.L., K.K., J.W.K., and T.D. performed experiments and data analysis. J.J., and N.H.P. supervised human biopsies collection and analysis. M.K. and T.Y.L. performed histological and biochemical analysis of human biopsies. B.B., Y.K., H.L., and D.N. performed the bioinformatics analysis. Y.R.Y., H.Y., D.R., S.H., and W.K.K. participated in the discussion and validation. Y.H.L., J.L., J.J., K.P., and J.H.C wrote the manuscript. All authors discussed the experiments, revised and approved the manuscript.

Corresponding Authors

Neung Hwa Park, Hwayoung Yun, and Jang Hyun Choi

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Hepatic miR-93 promotes the pathogenesis of metabolic dysfunction-associated steatotic liver disease by suppressing SIRT1

Status:

Version 1

Abstract

Figures

Introduction

Results

miR-93 is significantly upregulated in livers of human patients with MASLD and diet-induced obese mice

miR-93 deficiency attenuates hepatic inflammation and fibrosis in MASH

miR-93 modulates cholesterol and lipid metabolism in the liver

SIRT1 is a direct target of miR-93

miR-93 promotes hepatic steatosis through SIRT1 modulation

Discussion

Methods

Human patients

Cell culture

Mice

Analysis of the publicly available hepatic transcriptome with human and mouse

RNA sequencing and bioinformatics analysis

Differential gene expression analysis and functional annotation

miRNA regulatory network analysis

Immunoblotting

Quantitative PCR

Cellular oxygen consumption rate (OCR)

Production of Adeno-associated virus and lentivirus

Biochemical analysis

Histological analysis

Measurement of SIRT1 activity and NAD⁺ concentration

Statistical analysis

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1

Hepatic miR-93 promotes the pathogenesis of metabolic dysfunction-associated steatotic liver disease by suppressing SIRT1

Status:

Version 1

Abstract

Figures

Introduction

Results

miR-93 is significantly upregulated in livers of human patients with MASLD and diet-induced obese mice

miR-93 deficiency attenuates hepatic inflammation and fibrosis in MASH

miR-93 modulates cholesterol and lipid metabolism in the liver

SIRT1 is a direct target of miR-93

miR-93 promotes hepatic steatosis through SIRT1 modulation

Discussion

Methods

Human patients

Cell culture

Mice

Analysis of the publicly available hepatic transcriptome with human and mouse

RNA sequencing and bioinformatics analysis

Differential gene expression analysis and functional annotation

miRNA regulatory network analysis

Immunoblotting

Quantitative PCR

Cellular oxygen consumption rate (OCR)

Production of Adeno-associated virus and lentivirus

Biochemical analysis

Histological analysis

Measurement of SIRT1 activity and NAD+ concentration

Statistical analysis

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1

Measurement of SIRT1 activity and NAD⁺ concentration