Survival and Proliferation of Activated Hepatic Stellate Cells Requires REST

Survival and Proliferation of Activated Hepatic Stellate Cells Requires REST | 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 Survival and Proliferation of Activated Hepatic Stellate Cells Requires REST Vladimir Shavva, Laura Tarnawski, Wanmin Dai, Noah Moruzzi, Anne-Sophie Haller, and 15 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6237126/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 27 Jan, 2026 Read the published version in Molecular Medicine → Version 1 posted You are reading this latest preprint version Abstract The activation of hepatic stellate cells (HSCs) is a key driver of liver fibrosis and inflammation in metabolic dysfunction-associated steatotic liver disease (MASLD). Targeting activated HSCs has shown promise in preventing liver fibrosis and cancer in mouse models. HSC activation is characterized by increased mitochondrial metabolism and upregulation of pro-fibrotic genes. Since RE1-silencing transcription factor (REST) is known to regulate cell fate, metabolism, and survival, we investigated its involvement in HSC activation. We observed reduced Rest mRNA levels in mouse activated HSCs as compared with mouse quiescent HSCs. In primary human HSCs and HSC-like LX2 cells, REST knock-down led to decreased expression of pro-fibrogenic markers and was essential for the survival and proliferation of activated human HSCs in vitro . REST knock-down also promoted lipid accumulation, impaired mitochondrial metabolism, and increased AMPK phosphorylation and autophagy, resulting in reduced growth. Our findings identify a REST-dependent mechanism of HSC activation that is important for their survival and proliferation. Biological sciences/Cell biology Health sciences/Diseases/Metabolic disorders Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Metabolic dysfunction-associated steatotic liver disease (MASLD) is a significant and growing public challenge with limited treatment options 1 , 2 . MASLD is characterized by excessive lipid accumulation in the liver and can progress to metabolic dysfunction-associated steatohepatitis (MASH) and liver cancer 1 , 2 . Patients with a diagnosis of MASLD are 17 times more likely to develop hepatocellular carcinoma (HCC) as compared with a matched control population 3 , and MASLD/MASH are expected to become the predominant cause of liver cancer 4 . Activation of hepatic stellate cells (HSCs) is key in the progression from MASLD to MASH and liver cancer: activated HSCs are the primary contributors to the development of liver fibrosis, and depletion of activated HSCs reduced both tumor number and size in multiple mouse models of HCC 1 , 2 , 5 – 7 . Although a number of clinical trials targeting activated HSCs with pleiotropic drugs were attempted in the past or are underway, there are no approved treatments targeting activated HSCs 8 . Therapeutic targeting of HSC activation, proliferation, and survival in MASLD/MASH is attractive but the mechanisms that regulate the HSC activation are not well understood. HSC activation involves loss of the quiescent gene expression profile with upregulation of pro-fibrotic and pro-inflammatory genes 1 , 2 and requires increased mitochondrial metabolism 9 – 11 . Quiescent HSCs populating the healthy liver are characterized by expression of neuron-associated genes such as synaptophysin (SYP) 12 – 14 , yet the mechanisms that regulate the neuron-associated functions in HSCs are largely unknown. Interestingly, the Repressor Element 1-silencing transcription factor (REST) is known to silence neuron-associated genes in non-neuronal cells 15 and to regulate the neuron-associated choline acetyltransferase (ChAT) in primary human T cells 16 . Regulation of gene expression by REST depends on its expression level: downregulation of REST in stem cells allows for induction of neuron-specific genes in differentiating neurons 17 . However, although REST expression is reduced in neurons compared to somatic cells, REST is key for neuronal function and survival: REST dysfunction in neurons leads to cell senescence and death, and is linked to development of Alzheimer’s and Parkinson’s diseases, and reduced lifespan 17 – 20 . Together, these observations raise the question of whether REST regulates aspects of HSC activation. In this study, we investigated REST in HSC activation and found that REST is vital for survival and proliferation of activated human HSCs in vitro . REST knock-down affected mitochondrial metabolism and caused lower mitochondrial membrane potential, induced AMPK phosphorylation and activation of PI3K cascades and autophagy, abrogating activated HSC growth and promoting death. Accordingly, REST emerges as a key factor in HSC activation and a potential molecular target to abrogate expansion and pro-fibrogenic activity of activated HSC in liver disease. Results Rest was reduced in activated HSCs Analysis of single cell RNA sequencing data of HSCs from three different experimental models of liver injury, bile duct ligation (BDL) 21 , chronic CCl 4 injections (GSE171904 21 ), and 12-week western diet 22 showed that transcript levels of Rest in HSCs were reduced in experimental MASH (Fig. 1 a,b, Table S1 ), and in activated HSCs as compared with quiescent HSCs (Fig. 1 c,d, Table S1 ). Rest levels were similar in portal vein- and central vein-associated HSCs (Fig. S1 a, b, Table S1 ). Uniform manifold approximation and projection (UMAP) plot of Rest + activated HSCs in the GSE206409 5 dataset showed similar levels of Rest (log 2 FC = 0.13) in cytokine-producing HSCs (cyHSCs) and myofibroblast HSCs (myHSC) (Fig. 1 e, Table S1 ). To study Rest -associated gene networks in quiescent HSCs and activated HSCs, we obtained data from experimental MASH analyzed by scRNA sequencing 22 and performed high dimensional weighted gene co-expression network analysis (hdWGCNA) 23 . The analysis identified seven distinct sets of genes, ‘co-expression modules’ 23 , each designated by a color (Fig. 1 f). The highest eigengene-based connectivity (kME) for Rest appeared in the co-expression module labelled ‘blue’ (Fig. 1 f, g, S1c). Transcript levels of the genes contained in this module were significantly lower in activated HSCs as compared with quiescent HSCs (Fig. 1 h, Fig. S1 c). Pathway analysis revealed that the ‘blue’ module transcripts were associated with regulation of gene expression, cell motility and proliferation, and nervous system development (Fig. S1 e). Further analysis of the top 25 genes as sorted by topological overlap with Rest levels revealed enrichment for transcripts associated with neurotransmitter and glucocorticoid signaling, and secretion (Fig. S1 d, f). To evaluate the robustness of the identified modules across datasets, we analyzed module preservation in the GSE171904 21 dataset of HSC activation in liver fibrosis (Fig. 1 i). Linkage of genes within the Rest -associated 'blue' module was observed across the datasets (Fig. 1 j, k), supporting the presence of the Rest -associated transcript network in HSCs across different mouse models of MASH. Together, the observations show an association between Rest and HSC activation. REST regulated HSC activation To study the function of REST in HSC activation, we activated primary human HSCs (phHSCs) for 12 days and exposed cultures to siRNA targeting REST (siREST) or scrambled siRNA throughout the activation period. qPCR and immunofluorescence showed significant REST knock-down (KD) by siREST (Fig. 2 a, b). Vitamin A-specific autofluorescence is lost upon HSC activation 1 , 24 , and phHSCs had significantly higher cellular autofluorescence following REST KD as compared with exposure to scrambled siRNA (Fig. 2 c), as measured by flow cytometry. Furthermore, REST KD strongly upregulated the transcript levels of synaptophysin ( SYP ), an established HSC marker 12 and direct target of REST 25 , and significantly downregulated pro-fibrogenic collagen 1α1 ( COL1A1 ) and the activated HSC marker α smooth muscle actin ( ACTA2 ) (Fig. 2 d). Similarly, REST KD in the human hepatic stellate cell line LX2 26 (Fig. S2 a,b) reduced COL1A1 and elevated SYP (Fig. S2 c). Further, both REST KD and inhibition of activation using 72h serum starvation reduced transcript levels of COL1A1 in LX2 cells (Fig. 2 e). Of note, serum starvation did not further reduce COL1A1 levels beyond REST KD, indicating that serum starvation downregulated COL1A1 by a mechanism that requires REST (Fig. 2 e). Thus, REST KD reduced the mRNA levels of the key pro-fibrogenic gene COL1A1 in activated HSCs. We next investigated the role of REST in the short-term response of activated HSCs to re-exposure to pro-activation microenvironments. In activated phHSCs exposed to fresh FBS (10%), REST KD promoted a significant upregulation of ACTA2 and SYP transcripts but caused no significant change in the level of COL1A1 (Fig. 2 f). Similarly, in cultures of activated LX2 cells, REST KD did not abrogate the TGFβ-dependent induction of COL1A1 . Of note, REST KD reduced the relative level of COL1A1 (Fig. 2 g, Fig. S2 d), corroborating REST-dependent regulation of COL1A1 . Together, these data indicate that although REST KD reduces COL1A1 expression, re-exposure of activated HSCs to a pro-activation microenvironment can upregulate COL1A1 . Next, we asked whether levels of COL1A1 and SYP protein are regulated by REST in activated HSCs. REST KD reduced secretion of COL1A1 from activated phHSCs in cell cultures from three out of four donors (Fig. 2 h), suggesting that REST promotes activated HSC-mediated fibrogenesis. Levels of SYP were higher in phHSCs and LX2 cells exposed to siRNA targeting REST as compared with scrambled siRNA (Fig. 2 i, S2e). Surprisingly, intracellular COL1A1 levels were increased after REST KD in both phHSCs (Fig. 2 i, Fig. S2 f) and LX2s (Fig. 2 j, k, Fig. S2 e, g) as measured by confocal microscopy immunofluorescence and flow cytometry, respectively, suggesting that REST KD regulates synthesis and secretion of COL1A1 independently. Together, these results indicate that REST regulates the in vitro pro-fibrogenic activity of activated HSCs. REST regulated HSC proliferation HSC survival and proliferation are key in the pathophysiology of MASH and activation of HSCs is known to induce proliferation 1 , 2 . We found that cell numbers were significantly lower in cell cultures following REST KD as compared with exposure to scrambled siRNA, both in phHSC and LX2 cell cultures during activation (Fig. 3 a, b). Accordingly, we hypothesized that REST controls proliferation of activated HSCs. Activated phHSCs with and without REST KD were studied using RNA-sequencing and gene set enrichment analysis (GSEA). Proliferation-related GO terms were selected from the identified pool of significantly regulated gene sets. We observed that pathways linked to proliferation were downregulated in phHSCs after REST KD (Fig. 3 c). To gain more insight, we asked how REST KD affected mRNA levels of genes included in the gene set with the lowest normalized enrichment score among these pathways, i.e. GO:0006261 “DNA-templated DNA replication”. Analysis across four independent donor cultures showed that REST KD consistently reduced transcript levels across the GO:0006261 gene set (Fig. 3 d). Further, the top 10 most differentially expressed transcripts from the GO sets related to proliferation (Fig. 3 c) were similarly downregulated following REST KD (Fig. 3 e). Accordingly, we proceeded to investigate whether REST KD controls HSC proliferation. To test this, we used an IncuCyte® live cell imaging system to measure HSC growth with and without concomitant serum starvation. We knocked down REST in phHSCs during activation for nine days and subsequently cultured the cells in the IncuCyte® live cell imaging system for an additional three days in media supplemented with 10% or 0% FBS. Subsequent analysis revealed that REST KD reduced the confluence of activated HSC to a similar extent as did serum starvation (Fig. 3 f-h). Further, REST KD LX2 cells had significantly higher levels of CellTrace fluorescence intensity after 96h of culture as compared with LX2 cells exposed to scrambled siRNA (Fig. 3 i), indicating reduced cell division. Together, these results show that REST regulates proliferation of activated HSCs. REST regulated HSC survival Next, we investigated REST in the context of activated HSCs survival. REST KD promoted phHSC death with an effect size similar to that of serum starvation (Fig. 4 a), and in LX2 cells to an even higher extent than serum starvation (Fig. 4 b,c). To study mechanism, we next asked if REST KD promotes HSC apoptosis. REST KD increased caspase-3 cleavage in LX2 cells (Fig. 4 d-f), and exposure of LX2 cells to the caspase inhibitor Z-VAD-FMK attenuated REST KD-mediated cell death (Fig. 4 g), identifying a role for apoptosis in REST KD-driven cell death. In contrast, exposure of LX2 cultures to Z-VAD-FMK did not significantly impact cell growth (Fig. 4 h) with or without REST KD, indicating that inhibition of REST KD-driven apoptosis is not sufficient to restore growth of activated HSCs, and that other mechanisms control REST-dependent proliferation. REST has previously been linked with PI3K 27 – 30 and mTOR 31 cascades in a context- and cell type-dependent manner 32 . The PI3K/AKT/mTORC1 cascade is known to be activated by growth factors and to inhibit cell death, promote proliferation, and to constitute a critical component in HSC activation 33 – 37 . We observed elevated AKT phosphorylation following REST KD in LX2 cells (Fig. 4 i), supporting a link between REST and the PI3K signaling cascade in activated HSCs. To study the mechanism behind REST -driven growth and survival of activated HSC, we exposed LX2 cells to the PI3K inhibitor LY294002. LY294002 exposure increased cell death by 59 ± 2% in scrambled siRNA-transfected cells, and by 35 ± 7% in REST KD cultures (Fig. 4 j,k), suggesting that at least part of the REST-driven regulation of LX2 survival requires PI3K cascade activity. In contrast, LY294002 exposure did not significantly suppress LX2 growth following REST KD as measured in an IncuCyte® live cell imaging system (Fig. 4 l,m). Further, inhibition of PI3K activity did not significantly affect REST-dependent regulation of either mRNA levels (Fig. S3a) or intracellular protein levels (Fig. S3b) of COL1A1, suggesting that REST-driven proliferation, survival and fibrogenesis are regulated by distinct downstream signaling mechanisms. To further interrogate REST- mediated regulation of the PI3K signaling cascade, we asked whether expression of the PI3K subunit p85α (PIK3R1), a key factor in HSC activation 37 , was regulated by REST . PIK3R1 mRNA levels were significantly reduced by REST KD in phHSCs (Fig. S3c) and LX2 cells (Fig. S3d, e). Accordingly, we knocked down REST , PIK3R1 , RAPTOR ( RPTOR ) or combination thereof in LX2 cells (Fig. S3f) and measured cell growth and cell death. Knock down of PIK3R1 or RPTOR had no significant effect on survival of LX2 cells during serum starvation or REST KD-mediated LX2 cell death (Fig. S3g). Further, inhibition of mTORC1 using rapamycin attenuated cell division in LX2 cells exposed to either siRNA targeting REST or scrambled siRNA as measured by CellTrace fluorescence (Fig. 4 n). Of note, the relative attenuation was significantly lower in REST KD cells (Fig. 4 o). Similarly, in REST KD LX2 cells, there was no additional reduction of cell growth following knock down of PIK3R1 or RPTOR (Fig. S3h), supporting the notion that PIK3R1/mTORC1 cascades and REST are linked in suppression of LX2 growth. Interestingly, PIK3R1 KD fully reversed downregulation of COL1A1 expression following REST KD (Fig. S3i), indicating that REST KD-driven suppression of COL1A1 expression was mediated in a PIK3R1-dependent manner. Together, the data suggests that REST KD-dependent regulation of activated HSC fibrogenesis, growth and survival is partially mediated by the PI3K/mTORC1 cascade. PIK3R1 was required for REST KD-driven COL1A1 suppression, but REST KD-driven death was not abrogated by PI3K/mTORC1 inhibition, further supporting the notion that REST regulates key aspects of HSC biology by discrete mechanisms. REST regulates metabolism in activated HSCs REST has been linked to metabolic regulation of neuronal function 38 , 39 and metabolic reprogramming is key for HSC activation 1 , 2 . Based on our observation that REST KD exacerbated the effect of serum starvation on growth and promoted death in phHSCs and LX2 cells (Fig. 3 , 5 a, S4a), we proceeded to study REST in HSC mitochondrial function. Since mitochondria are the key energy source driving HSC activation 1 , 2 , 9 , we asked whether REST was involved in the regulation of mitochondrial metabolism in activated HSCs. Analysis of the oxygen consumption rate (OCR) revealed that the basal respiration was elevated in LX2 cells with REST KD (Fig. 5 b-d). The ratio of mitochondrial to genomic DNA was higher in REST KD (Fig. 5 e) as was MitoTracker Deep Red FM fluorescence (Fig. 5 f), indicating an increase in mitochondrial mass and mitochondrial numbers. ATP-linked respiration was unchanged while proton leakage was significantly higher in LX2 cells with REST KD, observations that were further supported by the decrease in coupling efficiency and the increase in the spare respiratory capacity (Fig. 5 c, d). In line with these findings, JC-1 staining 40 indicated that the mitochondrial membrane potential was lower following REST KD (Fig. 5 g). Further, dichlorofluorescin diacetate (DCFDA) staining 41 indicated reduced ROS production in REST KD LX2 cells as compared with LX2 cells exposed to scrambled siRNA (Fig. 5 h). Together, the data links REST with mitochondrial metabolism and growth in activated HSCs. Since mitochondria-dependent ATP production is key for HSC activation 1 , 2 , 9 , we next asked whether REST KD affected the energy balance. We observed elevated levels of AMPKα Thr172 phosphorylation following REST KD (Fig. 5 i), indicating an increased AMP/ATP ratio. Furthermore, LipidTOX red staining indicated that REST KD elevated levels of intracellular lipids in phHSCs (Fig. 5 j, Fig. S4b) and LX2s (Fig. 5 k). These observations are congruent with insufficient quantities of ATP for activation and reduced lipid utilization in REST KD HSCs. Of note, inhibition of PI3K by LY294002 exposure did not significantly affect REST KD-driven lipid accumulation in LX2 cells (Fig. 5 l), indicating that REST -mediated regulation of lipid metabolism in activated HSCs is independent of PI3K activity. Autophagy can act as an energy source during starvation, and REST protects neurons from death in part through regulation of autophagy 19 , 42 . Accordingly, we asked whether autophagy in HSC is affected by REST KD. Staining of phHSCs with monodansylcadaverine (MDC), a marker for autophagic vacuoles, showed a significant increase in MDC fluorescence intensity following phHSCs REST KD (Fig. 5 m), indicating that REST KD promoted autophagy. Further, REST KD promoted LC3a/b cleavage in LX2 cells (Fig. 5 n), although no accumulation of MDC-stained autophagic vacuoles was observed in LX2 REST KD (Fig. S4c). Since AKT/mTORC1 axis was reported to suppress autophagy by phosphorylation Ser757 of ULK1 43 and AMPK is known to induce autophagy and oppose AKT-driven inhibition of autophagy 43 , we next studied the effect of REST KD in this context. We observed that REST KD reduced phosphorylation of Ser757 of ULK1 in LX2 cells (Fig. 5 n), suggesting that AMPK activation in REST KD promoted autophagy and attenuated AKT/mTORC1-driven suppression of autophagy (Fig. 6 ). Together, the data indicates that REST KD disrupted mitochondrial metabolism in activated HSCs, stimulating autophagy, and suppressing activation. Discussion Here, we describe an essential role of REST in promoting activation of HSCs and sustaining their survival and growth. By analyzing scRNA sequencing data in mouse models of MASH, we found that the level of REST was linked with disease activity. In vitro experiments showed that REST regulates HSC activation, survival and proliferation, key processes in the pathophysiology of MASH and hepatocellular carcinoma. The cellular function of REST is known to change with its expression level 17 . Accordingly, the observation here that REST was reduced in HSCs in multiple models of MASH is interesting: REST is downregulated during the differentiation of neurons 17 and is critical for maintenance of neuronal function and health. Further downregulation of REST in neurons impairs function, promotes cell death and reduces lifespan 17 – 20 . Similarly, we found that REST expression in HSCs was reduced during activation. REST regulated key aspects of HSC activation, and further knock-down of REST negatively impacted activated HSC proliferation and survival. It is well established that collagen synthesis and release, cellular proliferation, and survival are key functions of HSC in the pathophysiology of liver fibrosis 1 , 2 . A plethora of extracellular signals promote various aspects of HSC activation through different mechanisms. For instance, TGFβ is a key pro-fibrogenic stimulus and growth factors such as PDGFβ are essential drivers of HSC proliferation 1 , 2 . The findings here indicate that REST functionally connects aspects of HSC activation as evidenced by the observations that REST KD reduced COLA1A expression and secretion, reduced proliferation, and promoted apoptosis. Paradoxically, although REST KD downregulated COL1A1 expression and secretion, intracellular levels of COL1A1 protein were elevated. This discrepancy could be explained by independent regulation of transcription, translation and protein secretion by REST. We speculate that reduced secretion of COL1A1 protein following REST KD could be the cause of accumulation of COL1A1 protein in intracellular depots. This accumulation may lead to ER stress and contribute to the elevated cell death we observed following REST KD. Interestingly, REST-dependent signaling cascades that regulate COL1A1 production, and survival and proliferation of activated HSC, were not interdependent. For instance, REST-driven survival depended on activity of the PI3K signaling cascade, but REST and PI3K independently regulated growth. It is, however, apparent that other mechanisms induced by REST KD opposed the effects of PI3K signaling, as both autophagy and cell death were promoted by REST KD while proliferation was impaired. First, PI3K is known to promote survival by opposing apoptosis 44 , but in REST KD we observed both increased AKT phosphorylation and apoptosis, indicating simultaneous induction of PI3K signaling and apoptosis. Accordingly, we hypothesize that REST promotes apoptosis in part through mechanisms that are not targeted by the PI3K cascade. Second, activation of the mTORC1 cascade was observed to inhibit autophagy 45 , and activation of the PI3K/AKT cascade promoted mTORC1 activity 46 , but we found simultaneous induction of PI3K activity and autophagy following REST KD. It is likely that REST KD shields the autophagic machinery from PI3K-driven inhibition by inducing AMPK activity. AMPK stimulates growth factor-driven activation of PI3K and AKT but diminishes AKT-driven activation of mTORC1 and opposes mTORC1-driven inhibition of autophagy 43 , 47 . We observed higher AMPK phosphorylation following REST KD, suggesting that change in energy metabolism balance after REST KD stimulates the PI3K/AKT cascade by inducing AMPK activity. It is possible that a similar mechanism inhibits anti-apoptotic activity of the PI3K/AKT cascade following REST KD. Importantly, neither PI3K nor apoptosis inhibition abrogated the reduction in cell growth following REST KD, indicating that REST regulates growth and death of activated HSCs through independent mechanisms. HSC activation involves profound changes in cell metabolism and these changes drive HSC activation 1 , 10 , 48 . ATP production through β-oxidation and mitochondrial respiration is a critical energy source for activated HSCs. Interestingly, loss of REST induced proton leakage in mitochondria and reduced coupling efficiency. In line with this, we observed higher AMPK phosphorylation following REST KD in LX2 cells. Further, we observed accumulation of lipids in activated HSCs following REST KD, suggesting that lipid utilization during activation was impaired in the absence of REST. These changes in lipid utilization, possibly due to mitochondrial dysfunction or metabolic rerouting within the cells, could cause reduced ATP levels following REST KD. Notably, we observed a similar accumulation of intracellular lipids following serum starvation. Serum drives activation of HSCs, causing activation-dependent utilization of lipids, and, accordingly, serum starvation abrogates this effect 1 , 2 . In contrast to the REST KD effects, we did not observe a reduction of mitochondrial membrane potential and elevated AMPK phosphorylation during serum starvation, suggesting that REST KD and serum starvation reduce HSC activation through different mechanisms. Together, these observations indicate that REST regulates cellular metabolic pathways for ATP production necessary to sustain HSC activation. In summary, our study uncovers REST as a novel factor required for HSC activation, growth and survival. Inactivation of REST in activated HSCs disrupts mitochondrial metabolism, promotes apoptosis and reduces COL1A1 expression, indicating that REST is a key factor linking activation-driven changes in activated HSC metabolism, growth and survival. The findings identify REST in activated HSCs as a novel potential therapeutic target in treatment of MASH. Materials and Methods Cell culture Primary human hepatic stellate cells Primary human hepatic stellate cells were purchased from LifeNet Health (USA), thawed, seeded at the density of 5000 cells/cm 2 , and cultured according to manufacturer’s instructions. Cells were transfected with scrambled siRNA (ThermoFisher, #4390843, Waltham, Massachusetts, USA) or siRNA targeting REST (Santa Cruz, #sc-38129, Dallas, Texas, USA) using reverse transfection with RNAiMAX (ThermoFisher, #13778-150). Complexes were prepared in Opti-MEM (ThermoFisher, #31985-047) and placed in culture plates. Cells were diluted in DMEM (ThermoFisher, #31966021) with GlutaMax and 20% FBS (ThermoFisher, #10270-106), placed into the wells containing complexes mixed. Cells were incubated in DMEM/Opti-MEM with GlutaMax and 10% FBS for 24h. Next, media was replaced with DMEM containing GlutaMax and 10% FBS. Cells were cultivated for 5 days, counted, passaged and re-transfected using the identical protocol. Immortalized human hepatic stellate cells (LX2) LX2 cells were maintained in DMEM with 10% FBS and seeded at the density of 21000 cells/cm 2 . Cells were transfected with scrambled siRNA (ThermoFisher, #4390843), siRNA targeting REST (Santa Cruz, #sc-38129), siRNA targeting PIK3R1 (ThermoFisher, #AM6708, assay ID 118085) or siRNA targeting RPTOR (ThermoFisher, #AM6708, assay ID 140660) using reverse transfection as described above. After 24h, media was replaced with DMEM containing GlutaMax and 10% FBS or DMEM with GlutaMax and without FBS for experiments involving serum starvation. For experiments involving TGFβ (Merck, #GF346, Germany), 24h after transfection media was replaced with DMEM containing GlutaMax without FBS. 24h later, media was replaced, and cells were exposed to 5 ng/mL of TGFβ. For experiments involving chemical inhibitors, cells were exposed to inhibitors 24h after transfection and incubated for 72h. For experiments involving rapamycin, cells were exposed to the inhibitor for 1h and media was replaced with fresh DMEM with 10% FBS. For all experiments, cells were harvested 96h after transfection. Generation of a stable LX2 cell line with an activation reporter gene The reporter construct indicating LX2 activation was a kind gift from Drs. Li and Xiang at the Peking University Health Science Center. Briefly, the COL1A1 promoter sequence was used to regulate the expression of the enhanced green fluorescent protein (EGFP) (pLVX-COL1A1-EGFP). The COL1A1 promoter was amplified, and the EF-1α promoter of plasmid pWPI was replaced with the COL1A1 promoter to obtain the recombinant plasmid pWPI-COL1A1. The internal ribosome entry site (IRES) sequence before the EGFP sequence of pWPI-COL1A1 was replaced with a Kozak sequence (GCCACCATGG) to obtain the recombinant plasmid pWPI-COL1A1-EGFP. The fragment consisting of the COL1A1 promoter, Kozak sequence, and EGFP was amplified, and replaced the CMV promoter and AcGFP1 on the pWVX-AcGFP1-N1 plasmid, resulting in the final recombinant plasmid pLVX-COL1A1-EGFP. Lentiviral vectors containing the LX2-activation reporter were packed in HEK293T cells using the pCMVdR8.91 and pMD2.G packaging plasmids according to standard protocols. LX2 cells were transduced using the lentiviral vectors overnight, and transduced cells were selected using 2.5 ug/mL puromycin for two days. For maintenance and subcluturing, cell culture media was supplemented with 0.5 ug/mL puromycin, which was withdrawn prior to experimentation. Incucyte Primary human hepatic stellate cells were thawed, seeded, transfected and cultured as described above. At day 6 post seeding, cells were trypsinized, counted and seeded on 96-well plates at the density of 1600 cells/well in DMEM + GlutaMax + 10% FBS. After 48h media was replaced with fresh DMEM + GlutaMax + 10% FBS or DMEM + GlutaMax, and cells were placed in Incucyte (Sartorius, Germany). LX2 cells were seeded in 96-well plate at the density of 3000 cells/cm 2 and transfected as described above. 24h after transfection, media was replaced with fresh DMEM + GlutaMax + 10% FBS, and cells were placed in Incucyte (Sartorius). For experiments involving chemical inhibitors, inhibitors were added to the media 24h after transfection. Cells were photographed every 4h for 72h. Analysis of confluence was performed with Incucyte 2022B Rev2 software using phase channel, AI confluence segmentation. For area under the curve (AUC) calculation, confluence was normalized to confluence at time point 0 per well. RNA isolation, reverse transcription and quantitative polymerase chain reaction (RT-qPCR) RNA from primary human hepatic stellate cells was isolated using RNeasy Mini Kit (Qiagene, #74104, Germany). RNA from LX2 cells was isolated using QIAzole (Qiagene, #79306). DNA digestion was performed using RNase-Free DNase (Qiagene, #79254). RNA concentration was measured using NanoDrop 2000 (ThermoFisher). 0.1 to 1 µg of RNA was used for RT. RT was performed using High-Capacity cDNA Reverse Transcription Kit (ThermoFisher, #4368814). qPCR was performed in QuantStudio 7 Pro (ThermoFisher) using GoTaq Probe qPCR kit (Promega, #A6102, Madison, Wisconsin, USA) for Taqman qPCR or SsoAdvanced Universal SYBR Green Supermix (Bio-Rad, #1725274, Hercules, California, USA) for SYBR Green qPCR and primers listed in Table S2 . mRNA levels were calculated as 2 Ct(control)−Ct(sample) and normalized to geometric mean of housekeeping genes mRNA level ( RPLP0 , PPIA , SDHA ). SeaHorse extracellular flux analysis Metabolic analysis was performed using the Seahorse XF964 Flux Analyzer (Agilent, Santa Clara, CA, U.S.). LX2 cells were transfected with scrambled siRNA or siRNA targeting REST as described. 24h before SeaHorse analysis LX2 cells were re-seeded in 96-well XF Cell Culture Microplates (40000 cells/well) in complete DMEM medium with 10% FBS and GlutaMax. 1h prior to the analysis the medium was replaced with XF medium containing 5,5 mM glucose, 2 mM GlutaMax (ThermoFisher), 1 mM sodium pyruvate. Cells were incubated at 37°C without CO2 for 1h prior to the experiment. The oxygen consumption rate (OCR) was recorded at basal level and following injection of oligomycin (1 uM final), carbonyl-cyanide 4-trifluoromethoxy-phenylhydrazone FCCP (0,5 + 0,5 µM), and mixture of rotenone and antimycin A (5 µM). Running template was 2´ mix and 4,5´ measure. All chemicals were purchased from Sigma-Aldrich. Data were normalized on the number of cells per well. Normalization for cell number was carried out staining the nuclei with Hoescht 33342 (Molecular Probes, Eugine, Oregon, USA) for 10´ and then imaging each well using BD pathway 855 (BD Biosciences, Franklin Lakes, U.S.) with 10x objective and montage 5x4. Cell number was counted with Cell profiler software. Western blotting & ELISA LX2 cells were lysed in RIPA buffer with protease/phosphatase inhibitors (ThermoFisher, #1861281) (10 min, +4C). Protein concentration was determined using DC protein assay (Bio-Rad, #5000111) or Bredford assay (Bio-Rad, #500 − 0205). Proteins were segregated in 8–15% polyacrylamide gels and transferred onto PVDF membranes using wet transfer. Protein loading was determined using Ponceau Red S staining. Membranes were blocked with 5% blocking reagent (Cell Signaling, #9999S, Denver, Massachusetts, USA) or 5% BSA diluted in PBS/0.05% Tween20, stained with primary (Table S3) and secondary (Table S4) antibodies, and secondary antibody fluorescence was measured using ChemiDoc (Bio-Rad). COL1A1 concentration in media was measured using ELISA (R&D Systems, #DY6220-05) according to manufacturers instructions. Flow cytometry Primary HSC were cultured as described above. Following trypsinization, cells were washed in PBS -/- with 10% FBS. Prior to antibody staining, cells were incubated in the blocking buffer containing PBS -/- and 10% FBS. Cells were stained using primary conjugated antibodies (Table S5) in (a) PBS -/-supplemented with 10% FBS, (b) LipidTox Red (ThermoFisher, #H34476) and 1 µM monodansylcadaverine (Sigma, #D4008) or (c) 50 nM MitoTracker Deep Red FM (ThermoFisher, #M22426) and 10 µg/mL JC-1 (ThermoFisher, #T3168) in PBS -/- for 30 minutes at 4C (a) or room temperature (b,c). Viability was assessed using Zombie Yellow™ Fixable Viability Kit (BioLegend, #423102), 3 µg/mL propidium iodide (ThermoFisher, #P3566) or 1 µg/mL DAPI. For staining with antibodies against intracellular proteins, cells were fixed and permeabilized according to manufacturer protocol using BD Cytofix/Cytoperm™ Fixation/Permeabilization Kit (BD, #554714). For detection of ROS, cells were stained with 2’,7’-dichlorofluorescin diacetate (Millipore, #287810, Burlington, Massachusetts, USA) for 30 min. at + 37C, washed with HBSS and fixed with 10% methanol/HBSS for 5 min. at RT. The gating strategies for flow cytometry are shown in Figures S5, S6, S7. Stained cells were analyzed using the Cytek Aurora 5 laser system (Cytek) and FlowJo software v.10 (FlowJo). Immunocytochemistry and confocal microscopy Primary HSCs (4250 cells/cm 2 ) and LX2 cells (25000 cells/cm 2 ) were cultured on Ibidi 18-well µ-Slide Cells were transfected with siRNA as described above. Cells were fixed in 2% PFA/PBS, blocked with 1% BSA in 0.1% Tween-20/PBS and stained with antibodies (Table S3) overnight at + 4C. Cells were washed with PBS and stained with secondary antibodies for 1h at + 4C. Next, cells were washed with PBS, stained with DAPI (1 ug/mL) and maintained in 10% glycerol/PBS at + 4C. Images were acquired with a confocal laser scanning microscope (Nikon, ECLIPSE Ti2) equipped with 10x (N/A = 0.45) and 20x (N/A = 0.75) objectives and NIS-Elements Software (version 5.11.01). For quantification of COL1A1 and αSMA immunofluorescence, three to five fields of view (10x objective, 1269.76 x 1269.76 µm) were acquired per donor per condition using the same settings. Images were analyzed using QuPath software (version 0.5.1). Cell area was determined by the area above a set threshold. Total fluorescent signals of each cell were calculated by the mean fluorescent intensity of the cell times the area. CellTrace CellTrace™ cell proliferation assay (ThermoFisher, #C34564) was performed according to the manufacturer’s instructions. Briefly, following trypsinization, LX2 cells were resuspended in the CellTrace working solution (1 µM). Cells were incubated for 20 minutes at 37°C. The reaction was quenched by DMEM with 2% FBS for 5 minutes at RT. Next, 4x10 4 cells per well were plated in a 24-well plate and transfected with siRNA as described above. After 24 h media was changed to DMEM + GlutaMax with 10% FBS. 96 hours after plating cells were trypsinised and fixed with 1% PFA. CellTrace accumulation was measured using Cytek Northern Lights flow cytometer (Cytek Biosciences, #NL-3000, Fremont, California, USA) and analyzed using FlowJo software version (10.10.0). Fluorimetric Live/Dead assay Fluorimetric measurement of cell death was performed using Cell Viability Assay Kit (Abcam, #ab112121, UK) according to manufacturers instructions. Briefly, green and red fluorescent dyes were diluted in PBS. Media was removed and LX2 cells were loaded with dyes for 1h at + 37C. Fluorescence (490 ± 9 nm/525 ± 8 nm and 540 ± 12 nm/620 ± 20 nm) was measured at well bottom with spiral averaging using VANTAstar fluorimeter (BMG Labtech, Germany). Dead cell-derived fluorescence was normalized with green fluorescence and presented relative to control samples as arbitrary units (AU). Mitochondrial/Genomic DNA ratio Total DNA was extracted using Genomic DNA GeneJET Purification Kit (ThermoFisher, #K0721). Mitochondrial DNA was quantified using primers for tRNA-Leu (5-CACCCAAGAACAGGGTTTGT-3, 5-TGGCCATGGGTATGTTGTTA-3) 49 , genomic DNA was quantified using primers for GAPDH pseudogene (5- AAGGGCATCCTGGGCTAC-3, 5-GTGGAGGAGTGGGTGTCG-3) and SsoAdvanced Universal SYBR Green Supermix (Bio-Rad, #1725274). Single cell RNA sequencing analysis Publicly available data from single cell RNA sequencing datasets featuring primary mouse HSCs (datasets GSE17190419 21 , GSE206409 5 and Rosenthal et al., 2021 22 ) were analyzed using R/Bioconductor package Seurat 50 . Gene expression analysis was performed using Seurat function FindMarkers. Gene Ontology and Gene Set Enrichment analysis was performed using R package clusterProfiler 51 . High dimension weighted gene co-expression network analysis (hdWGCNA) was performed using the R package hdWGCNA 23 . RNA-sequencing Primary human HSCs were activated by plating for 12 days in DMEM containing 10% FBS and transfected with scrambled siRNA or siRNA targeting REST. Cells were exposed to fresh DMEM with 10% FBS 24h before RNA extraction. RNA was extracted using RNeasy Mini Kit (Qiagene, #74104) with DNA digestion (Qiagene, #79254). Pair-end mRNA sequencing of poly-A enriched RNA with the depth of 90 million reads was performed at Novogene. Alignment to human transcriptome (release hg38) and quantification was performed with Salmon 52 . Data was imported 53 and analyzed with DESeq2 54 . Heatmaps were plotted using pheatmap. Pathway analysis was performed with clusterProfiler 51 . Statistics and reproducibility Statistical analysis was performed using the R package ggpubr. Normality testing was performed using the Shapiro test. Pair-wise comparison was performed using t test or Mann-Whitney U test followed by multiple comparison correction using Hommel test or FDR test. Statistical tests are indicated in figure legends. Interquartile range method was used for outlier detection. Outliers are shown as blue dots. P < 0.05 was considered significant. Acknowledgements & Funding The reporter construct pLVX-COL1A1-EGFP was a kind gift from Dr. Li and Dr. Xiang at the Peking University Health Science Center. We thank Dr. Alda Saldan (Biomedicum FACS Facility, Karolinska Institutet) for the aid with experiments evaluating mitochondrial membrane potential. PSO is supported by the Swedish Research Council (Grant #2020–04443, #2024–03735), Heart-Lung Foundation (Grant #20200827, #20210524, #20230006), Karolinska Institutet (Grant #2023 − 01461, and #FoUI-987284). PSG is supported by Åke Wibergs Stiftelse (Grant #M24-0065), the European Atherosclerosis Society, KI-fonder (Grant #2024–02617), and Karolinska Institutet Network Medicine Alliance (Grant #2-776/2024). OA was supported by Novo Nordisk post doctoral fellowship and Stiftelsen Professor Nanna Svartz fond (Grant #2023–02). Declarations Conflict of Interest Peder S Olofsson is a shareholder of Emune AB. The other authors have declared no financial or other conflicts of interest related to this submission. Contributions VSS, OA and PSO conceived the project. VSS, WD, LT, NM, ASH, SH, FB, QG, MC, JJV performed experiments and analyzed the data. PSG created the pLVX-COL1A1-EGFP-expressing LX2 cells. VSS, PSO, LT, WD wrote the manuscript. All authors read, edited and approved the manuscript. Ethics Approval This study did not require ethical approval. Availability of Data and Materials The data generated in this study (RNA-sequencing of phHSCs) has been deposited in the Gene Expression Omnibus (GEO) database with accession number GSE289433. The code will be made publicly available at https://github.com/ImmunoBioLab/Shavva2025 following publication. References Tsuchida, T. & Friedman, S. L. Mechanisms of hepatic stellate cell activation. Nat. Rev. Gastroenterol. Hepatol. 14, 397–411 (2017). Liu, X., Xu, J., Brenner, D. A. & Kisseleva, T. Reversibility of Liver Fibrosis and Inactivation of Fibrogenic Myofibroblasts. Curr. Pathobiol. Rep. 1, 209–214 (2013). Simon, T. G. et al. 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Additional Declarations There is a duality of interest Supplementary Files 20250314ShavvaSupplementaryinformationWB.docx Supplementary information Western Blots 20250314ShavvaSupplementaryinformation.docx Supplementary information FigureS1.tif S1 FigureS2.tif S2 FigureS3.tif S3 FigureS4.tif S4 FigureS5.tif S5 FigureS6.tif S6 FigureS7.tif S7 Cite Share Download PDF Status: Published Journal Publication published 27 Jan, 2026 Read the published version in Molecular Medicine → 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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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-6237126","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":431489393,"identity":"c8d92655-9bbc-4982-9f42-1a4d35d56615","order_by":0,"name":"Vladimir 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GSE171904, GSE206409 and Rosenthal et al., 2021 datasets was analyzed as described in Methods. \u003cstrong\u003e(a-d)\u003c/strong\u003e Violin plots of Rest expression. Mice were exposed to CCl\u003csub\u003e4\u003c/sub\u003e injections or bile duct ligation (BDL) (GSE171904) or western diet for 12 weeks (Rosenthal et al., 2021). \u003cem\u003eRest\u003c/em\u003e expression was compared in Rest\u003csup\u003e+\u003c/sup\u003e cells using Wilcox test, FDR test for multiple comparisons correction. \u003cstrong\u003e(e)\u003c/strong\u003e Violin plot and Uniform manifold uapproximation and projection (UMAP) of Rest expression in the GSE206409 dataset. Rest+ activated HSCs are shown in blue. \u003cstrong\u003e(f-h)\u003c/strong\u003e High dimensional weighted gene co-expression network (hdWGCNA) analysis of co-expression networks from the Rosenthal et al., 2021 dataset\u003csup\u003e22\u003c/sup\u003e. \u003cstrong\u003e(f)\u003c/strong\u003e hdWGCNA dendrogram of gene co-expression network of genes expressed in HSCs. \u003cstrong\u003e(g)\u003c/strong\u003e Bar graph of Rest eigengene-based connectivity (kME) per module. \u003cstrong\u003e(h)\u003c/strong\u003e Dot plot of module eigengenes per module per cell type. \u003cstrong\u003e(i)\u003c/strong\u003e Dot plot of module eigengenes per module per cell type of the blue module generated from Rosenthal et al., 2021 dataset after application to GSE171904 dataset. \u003cstrong\u003e(j, k)\u003c/strong\u003e Scatter plots showing module preservation statistics between Rosenthal et al., 2021 dataset and GSE171904. Statistics were calculated using hdWGCNA. The x-axis shows module size. \u003cstrong\u003e(j)\u003c/strong\u003e The y-axis shows Z-score summary of quality. \u003cstrong\u003e(k)\u003c/strong\u003e The y-axis shows Z-score summary of preservation. ***p\u0026lt;0.001, ****p\u0026lt;0.0001.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-6237126/v1/afd8a54ed00f4f55971e9364.png"},{"id":78894250,"identity":"2c8804ba-5270-4f55-b63c-30636648b679","added_by":"auto","created_at":"2025-03-20 11:57:20","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":4001323,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eREST regulated activation of primary human HSCs.\u003c/strong\u003e \u003cstrong\u003e(a-e, h, j) \u003c/strong\u003ePrimary human HSCs were activated by plating and transfected with scrambled siRNA or siRNA targeting \u003cem\u003eREST\u003c/em\u003e for 12 days. \u003cstrong\u003e(a, d)\u003c/strong\u003e qPCR. Dots show mRNA level of genes of interest in activated phHSCs normalized to \u003cem\u003ePPIA\u003c/em\u003e, \u003cem\u003eRPLP0\u003c/em\u003eand \u003cem\u003eSDHA \u003c/em\u003e(n=4). Colors indicate HSCs from individual donors (paired \u003cem\u003et\u003c/em\u003etest). \u003cstrong\u003e(b)\u003c/strong\u003e Confocal microscopy. Representative images of REST protein level in activated phHSC (n=4). \u003cstrong\u003e(c)\u003c/strong\u003e Flow cytometry analysis of vitamin A-derived autofluorescence in live phHSCs (n=7). Dots show mean fluorescence intensity of sample normalized to mean of the samples transfected with scrambled siRNA (paired \u003cem\u003et\u003c/em\u003e test). \u003cstrong\u003e(e)\u003c/strong\u003e\u003cem\u003e \u003c/em\u003eqPCR. \u003cem\u003eCOL1A1 \u003c/em\u003eexpression in LX2 cells following 96h transfection with scrambled siRNA or siRNA targeting REST. Cells were exposed to media containing 10% or 0% FBS for 72h (n=3). Bars show mean±SEM, normalized to \u003cem\u003ePPIA\u003c/em\u003e, \u003cem\u003eSDHA\u003c/em\u003e and \u003cem\u003eRPLP0\u003c/em\u003e and expressed relative to mean of scrambled siRNA-transfected cells (\u003cem\u003et\u003c/em\u003e test, \u0026nbsp;Hommel test for multiple comparisons correction). \u003cstrong\u003e(f)\u003c/strong\u003e RNA-sequencing. mRNA levels of genes of interest in activated phHSCs (n=4). Cells were exposed to fresh DMEM with 10% FBS for 24h. y axis shows transcripts per million (TPM).\u003cstrong\u003e \u003c/strong\u003eColors indicate HSCs from individual donors (t test). \u003cstrong\u003e(g)\u003c/strong\u003e qPCR. \u003cem\u003eCOL1A1 \u003c/em\u003eexpression in LX2 cells following 96h transfection with scrambled siRNA or siRNA targeting REST. 24h after transfection, cells were serum starved for 24h and exposed to 5 ng/mL of TGFβ for 48h (n=4). Bars show mean±SEM, normalized to \u003cem\u003ePPIA\u003c/em\u003e, \u003cem\u003eSDHA\u003c/em\u003e and \u003cem\u003eRPLP0\u003c/em\u003e and expressed relative to mean of scrambled siRNA-transfected cells (\u003cem\u003et\u003c/em\u003etest, \u0026nbsp;Hommel test for multiple comparisons correction). \u003cstrong\u003e(h)\u003c/strong\u003e Quantification of COL1A1 protein secretion from activated phHSCs by ELISA (n=4). Dots show REST protein level. Colors indicate HSCs from individual donors (paired \u003cem\u003et\u003c/em\u003e test). \u003cstrong\u003e(i)\u003c/strong\u003e Confocal microscopy. Representative images of COL1A1, αSMA and SYP protein level in phHSCs (n=4). Merged – images showing nuclear staining (DAPI) and protein stainings. \u003cstrong\u003e(j-k)\u003c/strong\u003e Flow cytometry. LX2 carrying EGFP under control of COL1A1 promoter were transfected with scrambled siRNA or siRNA targeting REST for 96 hours. LX2 – non-transfected cells. \u003cstrong\u003e(j)\u003c/strong\u003e Representative histogram of GFP fluorescence (n=2). y axis shows cell count normalized to mode. \u003cstrong\u003e(k)\u003c/strong\u003e Bar graph showing GFP fluorescence (n=2). The Y axis shows MFI (mean fluorescence intensity) normalized to scrambled siRNA-transfected cells (Mann-Whitney \u003cem\u003eU\u003c/em\u003e test, Hommel test for multiple comparisons correction). NT – non-transfected. Outliers are indicated in blue. *p\u0026lt;0.05, **p\u0026lt;0.01.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-6237126/v1/b20b353d936f7099601ff728.png"},{"id":78894480,"identity":"8b8e8e4b-d38b-4e38-98a8-4b4178c6bc0b","added_by":"auto","created_at":"2025-03-20 12:05:20","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":4810567,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eREST regulates growth of primary human HSCs and LX2s. (a) \u003c/strong\u003ePrimary human HSCs were activated by plating and transfected with scrambled siRNA or siRNA targeting \u003cem\u003eREST\u003c/em\u003e for 6 days (n=8). Live cell counts were quantified using trypan blue staining (paired \u003cem\u003et\u003c/em\u003e test). \u003cstrong\u003e(b)\u003c/strong\u003e LX2 cells were transfected with scrambled siRNA or siRNA targeting REST for 96h (n=3). Live cell counts were quantified using trypan blue staining (\u003cem\u003et\u003c/em\u003e test). \u003cstrong\u003e(c-e)\u003c/strong\u003e RNA-sequencing analysis of phHSC transcriptomes following 12d activation and transfection (n=4). Cells were exposed to fresh DMEM with 10% FBS for 24h before harvesting. \u003cstrong\u003e(c)\u003c/strong\u003e Bar graph of normalized enrichment scores (NES) of GO terms related to proliferation. Gene Set Enrichment Analysis (GSEA) was performed with p value cutoff of 0.05. Color indicates adjusted p value. \u003cstrong\u003e(d)\u003c/strong\u003e Heatmap of transcripts per million (TPM) for genes belonging to GO:0006261 “DNA-templated DNA replication”. TPMs were normalized to average of TPMs per gene. \u003cstrong\u003e(e)\u003c/strong\u003e Volcano plot of top 10 most affected genes belonging to proliferation pathways shown in Fig. 3c. Horizontal dashed line indicates p value cutoff of 0.05, vertical dashed line indicates log\u003csub\u003e2\u003c/sub\u003e fold change cutoff of 0.5. \u003cstrong\u003e(f-h)\u003c/strong\u003e Analysis of phHSC growth using Incucyte (n=4). PhHSCs were transfected with scrambled siRNA or siRNA targeting REST. After 6 days cells were passaged, re-transfected and incubated with 10% FBS for 48h. Next, cells were placed into IncuCyte and media was changed to DMEM or DMEM + 10% FBS and photographed for 72h every 4h. \u003cstrong\u003e(f)\u003c/strong\u003e Representative images of primary human HSCs from D29 following 12d of activation and siRNA transfection. Yellow shows cell borders. \u003cstrong\u003e(g)\u003c/strong\u003e Change in confluence. Y axis shows confluence (%), and x axis shows time, donor ID indicated on top. \u003cstrong\u003e(h)\u003c/strong\u003e Area under the curve (AUC). Confluence was normalized to confluence at day 0 per well. Bars show mean AUC±SEM (Mann-Whitney \u003cem\u003eU\u003c/em\u003e test, FDR multiple comparison correction test). \u003cstrong\u003e(i)\u003c/strong\u003e Bar graph of LX2 proliferation (n=2) measure by flow cytometry. LX2 cells were loaded with CellTrace, transfected with scrambled siRNA or siRNA targeting REST for 96h. CellTrace accumulation was measured using flow cytometry (\u003cem\u003et\u003c/em\u003e test, Hommel test for multiple comparisons correction). *p\u0026lt;0.05, **p\u0026lt;0.01.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-6237126/v1/67c23d8a2638c2a17ce030ae.png"},{"id":78894242,"identity":"3e5c433b-f347-4d9f-9a7e-222e64e83aff","added_by":"auto","created_at":"2025-03-20 11:57:19","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1987593,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eREST regulates growth and survival of activated phHSCs and LX2 cells. (a) \u003c/strong\u003ePrimary human HSCs were activated by plating and transfected with scrambled siRNA or siRNA targeting \u003cem\u003eREST\u003c/em\u003e for 9 days and exposed to 10% or 0% FBS for 3 days.\u003cstrong\u003e (b-f, i) \u003c/strong\u003eLX2 cells were transfected with scrambled siRNA or siRNA targeting REST for 24h and exposed to 10% (b,d,e) or 0% (c,f,i) FBS for 72h. \u003cstrong\u003e(g, h, j-o)\u003c/strong\u003eLX2 cells were transfected with scrambled siRNA or siRNA targeting REST for 24h and exposed to DMSO (vehicle), Z-VAD-FMK (10 µM), LY294002 (10 µM) or rapamycin (5-10 nM) for 72h. \u003cstrong\u003e(a)\u003c/strong\u003e Bar graph of phHSC viability following activation and REST KD (n=4). Dead cells were quantified using zombie yellow staining and flow cytometry. Bars show mean±SEM % of dead cells from single cell gate, dots indicate individual donors (paired \u003cem\u003et\u003c/em\u003etest with FDR test for multiple comparisons correction). \u003cstrong\u003e(b, c, g, j)\u003c/strong\u003e Bar graph of LX2 viability after REST KD (b, e, g: n=2, c: n=6, j: n=4). Dead cells were quantified by propidium iodide (c) or zombie yellow (b, c, g, j) staining and flow cytometry. Bars show mean±SEM % of dead cells from single cell gate (Mann-Whitney \u003cem\u003eU\u003c/em\u003e test, Hommel test for multiple comparisons correction). \u003cstrong\u003e(d, e)\u003c/strong\u003e Flow cytometry. Representative histogram (d) and bar graph (e) of cleaved Caspase-3 fluorescence (n=2). Bars show mean±SEM % of cells from single cell gate (Mann-Whitney U test, Hommel test for multiple comparisons correction).\u003cstrong\u003e (f, i)\u003c/strong\u003e Western blotting of caspase-3 (f) or AKT (i) in LX2 cells (n=3). Representative images are shown. β-actin is shown as loading control. \u003cstrong\u003e(h, l)\u003c/strong\u003e Bar graph of relative LX2 growth (h, l: n=3). LX2 cells were incubated in Incucyte for 72h in DMEM with 10% FBS and photographed every 4h. Y axis shows area under the curve (AUC) normalized to mean of scrambled siRNA exposed to vehicle. Bar show mean±SEM in arbitrary units, AU (Mann-Whitney \u003cem\u003eU\u003c/em\u003etest, Hommel test for multiple comparison correction). \u003cstrong\u003e(k, m, o)\u003c/strong\u003e Bar graph showing ratios of REST siRNA-transfected samples to mean of scrambled siRNA-transfected samples (k, m: Mann-Whitney \u003cem\u003eU\u003c/em\u003etest, Hommel test for multiple comparisons correction; o:\u003cem\u003e t\u003c/em\u003e test, Hommel test for multiple comparisons correction). \u003cstrong\u003e(n)\u003c/strong\u003eBar graph of LX2 proliferation measure by flow cytometry. LX2 cells were loaded with CellTrace prior to transfection (n=2) (\u003cem\u003et\u003c/em\u003etest with Hommel test for multiple comparisons correction). Outliers are indicated in blue. *p\u0026lt;0.05, **p\u0026lt;0.01.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-6237126/v1/a9b62a7fef905741cc32d5f6.png"},{"id":78894256,"identity":"7c3c1012-8e8c-4a03-80c0-359515b5db80","added_by":"auto","created_at":"2025-03-20 11:57:20","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":2178281,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eREST regulates mitochondrial metabolism HSCs.\u003c/strong\u003e \u003cstrong\u003e(a-i, k, n)\u003c/strong\u003e LX2 cells were transfected with scrambled siRNA or siRNA targeting REST. 24h after transfection, cells were exposed to fresh medium containing DMEM with 10% FBS or 0% FBS for 72h. \u003cstrong\u003e(l)\u003c/strong\u003e LX2 cells were transfected with scrambled siRNA or siRNA targeting REST. 24h after transfection, cells were exposed to fresh medium containing DMEM with 10% FBS with DMSO (vehicle) or 10 µM LY294002 for 72h. \u003cstrong\u003e(j, m)\u003c/strong\u003e Primary human HSCs were activated by plating and transfected with scrambled siRNA or siRNA targeting REST for 12 days. 72h prior to harvesting cells were exposed to fresh media containing DMEM with 10% or 0% FBS. \u003cstrong\u003e(a) \u003c/strong\u003eBar graph of relative LX2 growth (n=3).\u003cstrong\u003e \u003c/strong\u003eLX2s were incubated in Incucyte for 72h. Photographs were taken every 4h. Y axis shows area under the curve (AUC) normalized to mean of scrambled siRNA exposed to 10% FBS (Mann-Whitney \u003cem\u003eU\u003c/em\u003e test, Hommel test for multiple comparison correction). \u003cstrong\u003e(b)\u003c/strong\u003eOxygen consumption rate (OCR) was measured in LX2 cells using SeaHorse. X axis shows time, y axis shows OCR (n=2). \u003cstrong\u003e(c-d)\u003c/strong\u003eBar graph showing OCR in LX2 cells in arbitrary units, AU (n=2). Samples were normalized to LX2 cells transfected with scrambled siRNA incubated in DMEM with 10% FBS (Mann-Whitney \u003cem\u003eU\u003c/em\u003e test, Hommel test for multiple comparison correction). \u003cstrong\u003e(e) \u003c/strong\u003eBar graphs of mitochondrial to genomic DNA ratio in LX2 cells (n=2). PCR. Samples were normalized to LX2 cells transfected with scrambled siRNA incubated in DMEM with 10% FBS (\u003cem\u003et\u003c/em\u003e test, Hommel test for multiple comparison correction). \u003cstrong\u003e(f)\u003c/strong\u003e Bar graphs of MitoTracker Deep Red FM fluorescence in LX2 cells (n=2). Flow cytometry. Samples were normalized to LX2 cells transfected with scrambled siRNA incubated in DMEM with 10% FBS (\u003cem\u003et\u003c/em\u003e test, Hommel test for multiple comparison correction). \u003cstrong\u003e(g)\u003c/strong\u003e Bar graphs of JC-1 red/green fluorescence ratio in LX2 cells (n=2). Flow cytometry. Samples were normalized to LX2 cells transfected with scrambled siRNA incubated in DMEM with 10% FBS (\u003cem\u003et\u003c/em\u003e test, Hommel test for multiple comparison correction). \u003cstrong\u003e(h)\u003c/strong\u003e Bar graphs of DCFDA fluorescence in LX2 cells (n=2). Flow cytometry. Samples were normalized to LX2 cells transfected with scrambled siRNA incubated in DMEM with 10% FBS (\u003cem\u003et\u003c/em\u003etest, Hommel test for multiple comparison correction).\u003cstrong\u003e (i, n)\u003c/strong\u003e Western blotting of AMPK (n=2), LC3A/B (n=4) and ULK1 (n=4) in LX2 cells. Representative images are shown. β-actin is shown as loading control. \u003cstrong\u003e(j)\u003c/strong\u003e Bar graph of LipidTOX Red fluorescence in phHSCs following activation for 12 days (n=4). Bars show MFI from single cell gate, and dots indicate individual donors (paired \u003cem\u003et \u003c/em\u003etest with FDR test for multiple comparisons correction). \u003cstrong\u003e(k, l)\u003c/strong\u003e Bar graph of LipidTOX Red staining of LX2s (n=2). Samples were normalized to LX2 cells transfected with scrambled siRNA incubated in DMEM with 10% FBS (\u003cem\u003et\u003c/em\u003e test, Hommel test for multiple comparison correction). \u003cstrong\u003e(m)\u003c/strong\u003eBar graph of monodansylcadaverine (MDC) staining of phHSCs (n=4). Dots indicate individual donors. Bars show mean±SEM. MFI – mean fluorescence intensity. AU – arbitrary units. Outliers are indicated in blue. *p\u0026lt;0.05, **p\u0026lt;0.01.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-6237126/v1/5671960cb7b95b540d2edde8.png"},{"id":78894490,"identity":"18f3ff9e-85a2-488b-b9b3-cfa425aafa9a","added_by":"auto","created_at":"2025-03-20 12:05:20","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":213213,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSchematic model of \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eREST\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e-dependent regulation of activated HSC survival and growth. (Left panel)\u003c/strong\u003e: \u003cem\u003eREST\u003c/em\u003e promotes mitochondrial function which maintains the level of energy essential for HSC activation. This, and other REST-dependent mechanisms contribute to activated HSC proliferation and pro-fibrogenic activity. \u003cstrong\u003e(Right panel)\u003c/strong\u003e: \u003cem\u003eREST\u003c/em\u003e KD disrupts mitochondrial metabolism, impairing ATP production, halting growth and promoting cell death. Lack of energy results in AMPK phosphorylation and activation, likely promoting autophagy, AKT phosphorylation and activation. However, the effects of AKT activation to inhibit autophagy and apoptosis are suppressed by \u003cem\u003eREST\u003c/em\u003e KD and AMPK activation.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-6237126/v1/4dbfacd321e6793e99476d65.png"},{"id":101513436,"identity":"f74d599c-a896-4c43-96cd-17baec83b387","added_by":"auto","created_at":"2026-01-30 15:36:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":11354269,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6237126/v1/2fbe9462-d3d0-4430-be6e-0039eecd7edb.pdf"},{"id":78894262,"identity":"3d552d41-e3d7-4c2c-abfe-e672e9e42897","added_by":"auto","created_at":"2025-03-20 11:57:20","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1884839,"visible":true,"origin":"","legend":"Supplementary information Western Blots","description":"","filename":"20250314ShavvaSupplementaryinformationWB.docx","url":"https://assets-eu.researchsquare.com/files/rs-6237126/v1/e3140368e98772deeedc8f6d.docx"},{"id":78894246,"identity":"0c3712c0-4eeb-45fe-94c7-33c6a3cac25a","added_by":"auto","created_at":"2025-03-20 11:57:20","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":18772961,"visible":true,"origin":"","legend":"Supplementary information","description":"","filename":"20250314ShavvaSupplementaryinformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-6237126/v1/8c6cb4fabade133963a14ecd.docx"},{"id":78895075,"identity":"267fe83a-d57c-4a01-9082-3f3874822a93","added_by":"auto","created_at":"2025-03-20 12:13:19","extension":"tif","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":3093151,"visible":true,"origin":"","legend":"\u003cp\u003eS1\u003c/p\u003e","description":"","filename":"FigureS1.tif","url":"https://assets-eu.researchsquare.com/files/rs-6237126/v1/6a970dbdb2ddc34889f99d84.tif"},{"id":78894497,"identity":"94d2bb4c-af61-47a7-9a1e-80e220be0f2a","added_by":"auto","created_at":"2025-03-20 12:05:21","extension":"tif","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":4714640,"visible":true,"origin":"","legend":"\u003cp\u003eS2\u003c/p\u003e","description":"","filename":"FigureS2.tif","url":"https://assets-eu.researchsquare.com/files/rs-6237126/v1/78e2aa69e039685374635a9d.tif"},{"id":78894287,"identity":"752cdf03-44f3-4d1f-b74f-2958ad7de93f","added_by":"auto","created_at":"2025-03-20 11:57:21","extension":"tif","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":1726965,"visible":true,"origin":"","legend":"\u003cp\u003eS3\u003c/p\u003e","description":"","filename":"FigureS3.tif","url":"https://assets-eu.researchsquare.com/files/rs-6237126/v1/e8693ec68bffb135ab18a3e8.tif"},{"id":78894260,"identity":"7322f250-2bf2-4542-a0ad-9a600a401069","added_by":"auto","created_at":"2025-03-20 11:57:20","extension":"tif","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":562782,"visible":true,"origin":"","legend":"\u003cp\u003eS4\u003c/p\u003e","description":"","filename":"FigureS4.tif","url":"https://assets-eu.researchsquare.com/files/rs-6237126/v1/f5006aa608eb9a195008e145.tif"},{"id":78894249,"identity":"2336ce73-c134-44cc-8839-6a65a2ac7bc0","added_by":"auto","created_at":"2025-03-20 11:57:20","extension":"tif","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":2648043,"visible":true,"origin":"","legend":"\u003cp\u003eS5\u003c/p\u003e","description":"","filename":"FigureS5.tif","url":"https://assets-eu.researchsquare.com/files/rs-6237126/v1/c10382f1e3c61405240bd39f.tif"},{"id":78894270,"identity":"55e6ab44-6075-4e4a-85da-be134ece12c2","added_by":"auto","created_at":"2025-03-20 11:57:20","extension":"tif","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":6563102,"visible":true,"origin":"","legend":"\u003cp\u003eS6\u003c/p\u003e","description":"","filename":"FigureS6.tif","url":"https://assets-eu.researchsquare.com/files/rs-6237126/v1/c18807c424fb2aad402a564e.tif"},{"id":78894291,"identity":"31b8122e-f364-4777-bfc9-94edc4e569a6","added_by":"auto","created_at":"2025-03-20 11:57:22","extension":"tif","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":7444868,"visible":true,"origin":"","legend":"\u003cp\u003eS7\u003c/p\u003e","description":"","filename":"FigureS7.tif","url":"https://assets-eu.researchsquare.com/files/rs-6237126/v1/fadfd42719a498e3106bd653.tif"}],"financialInterests":"There is a duality of interest","formattedTitle":"Survival and Proliferation of Activated Hepatic Stellate Cells Requires REST","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMetabolic dysfunction-associated steatotic liver disease (MASLD) is a significant and growing public challenge with limited treatment options\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. MASLD is characterized by excessive lipid accumulation in the liver and can progress to metabolic dysfunction-associated steatohepatitis (MASH) and liver cancer\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Patients with a diagnosis of MASLD are 17 times more likely to develop hepatocellular carcinoma (HCC) as compared with a matched control population\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e, and MASLD/MASH are expected to become the predominant cause of liver cancer\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Activation of hepatic stellate cells (HSCs) is key in the progression from MASLD to MASH and liver cancer: activated HSCs are the primary contributors to the development of liver fibrosis, and depletion of activated HSCs reduced both tumor number and size in multiple mouse models of HCC\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Although a number of clinical trials targeting activated HSCs with pleiotropic drugs were attempted in the past or are underway, there are no approved treatments targeting activated HSCs\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eTherapeutic targeting of HSC activation, proliferation, and survival in MASLD/MASH is attractive but the mechanisms that regulate the HSC activation are not well understood. HSC activation involves loss of the quiescent gene expression profile with upregulation of pro-fibrotic and pro-inflammatory genes\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e and requires increased mitochondrial metabolism\u003csup\u003e\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Quiescent HSCs populating the healthy liver are characterized by expression of neuron-associated genes such as synaptophysin (SYP)\u003csup\u003e\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e, yet the mechanisms that regulate the neuron-associated functions in HSCs are largely unknown. Interestingly, the Repressor Element 1-silencing transcription factor (REST) is known to silence neuron-associated genes in non-neuronal cells\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e and to regulate the neuron-associated choline acetyltransferase (ChAT) in primary human T cells\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Regulation of gene expression by REST depends on its expression level: downregulation of \u003cem\u003eREST\u003c/em\u003e in stem cells allows for induction of neuron-specific genes in differentiating neurons\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. However, although \u003cem\u003eREST\u003c/em\u003e expression is reduced in neurons compared to somatic cells, REST is key for neuronal function and survival: REST dysfunction in neurons leads to cell senescence and death, and is linked to development of Alzheimer\u0026rsquo;s and Parkinson\u0026rsquo;s diseases, and reduced lifespan\u003csup\u003e\u003cspan additionalcitationids=\"CR18 CR19\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Together, these observations raise the question of whether REST regulates aspects of HSC activation.\u003c/p\u003e \u003cp\u003eIn this study, we investigated REST in HSC activation and found that REST is vital for survival and proliferation of activated human HSCs \u003cem\u003ein vitro\u003c/em\u003e. \u003cem\u003eREST\u003c/em\u003e knock-down affected mitochondrial metabolism and caused lower mitochondrial membrane potential, induced AMPK phosphorylation and activation of PI3K cascades and autophagy, abrogating activated HSC growth and promoting death. Accordingly, REST emerges as a key factor in HSC activation and a potential molecular target to abrogate expansion and pro-fibrogenic activity of activated HSC in liver disease.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e \u003cb\u003eRest\u003c/b\u003e \u003cb\u003ewas reduced in activated HSCs\u003c/b\u003e\u003c/p\u003e \u003cp\u003eAnalysis of single cell RNA sequencing data of HSCs from three different experimental models of liver injury, bile duct ligation (BDL)\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e, chronic CCl\u003csub\u003e4\u003c/sub\u003e injections (GSE171904\u003csup\u003e21\u003c/sup\u003e), and 12-week western diet\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e showed that transcript levels of \u003cem\u003eRest\u003c/em\u003e in HSCs were reduced in experimental MASH (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea,b, Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e), and in activated HSCs as compared with quiescent HSCs (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec,d, Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). \u003cem\u003eRest\u003c/em\u003e levels were similar in portal vein- and central vein-associated HSCs (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003ea, b, Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Uniform manifold approximation and projection (UMAP) plot of \u003cem\u003eRest\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e activated HSCs in the GSE206409\u003csup\u003e5\u003c/sup\u003e dataset showed similar levels of \u003cem\u003eRest\u003c/em\u003e (log\u003csub\u003e2\u003c/sub\u003eFC\u0026thinsp;=\u0026thinsp;0.13) in cytokine-producing HSCs (cyHSCs) and myofibroblast HSCs (myHSC) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ee, Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). To study \u003cem\u003eRest\u003c/em\u003e-associated gene networks in quiescent HSCs and activated HSCs, we obtained data from experimental MASH analyzed by scRNA sequencing\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e and performed high dimensional weighted gene co-expression network analysis (hdWGCNA)\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. The analysis identified seven distinct sets of genes, \u0026lsquo;co-expression modules\u0026rsquo;\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e, each designated by a color (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ef). The highest eigengene-based connectivity (kME) for \u003cem\u003eRest\u003c/em\u003e appeared in the co-expression module labelled \u0026lsquo;blue\u0026rsquo; (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ef, g, S1c). Transcript levels of the genes contained in this module were significantly lower in activated HSCs as compared with quiescent HSCs (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eh, Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003ec). Pathway analysis revealed that the \u0026lsquo;blue\u0026rsquo; module transcripts were associated with regulation of gene expression, cell motility and proliferation, and nervous system development (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003ee). Further analysis of the top 25 genes as sorted by topological overlap with \u003cem\u003eRest\u003c/em\u003e levels revealed enrichment for transcripts associated with neurotransmitter and glucocorticoid signaling, and secretion (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003ed, f). To evaluate the robustness of the identified modules across datasets, we analyzed module preservation in the GSE171904\u003csup\u003e21\u003c/sup\u003e dataset of HSC activation in liver fibrosis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ei). Linkage of genes within the \u003cem\u003eRest\u003c/em\u003e-associated 'blue' module was observed across the datasets (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ej, k), supporting the presence of the \u003cem\u003eRest\u003c/em\u003e-associated transcript network in HSCs across different mouse models of MASH. Together, the observations show an association between \u003cem\u003eRest\u003c/em\u003e and HSC activation.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eREST\u003c/b\u003e \u003cb\u003eregulated HSC activation\u003c/b\u003e\u003c/p\u003e \u003cp\u003eTo study the function of \u003cem\u003eREST\u003c/em\u003e in HSC activation, we activated primary human HSCs (phHSCs) for 12 days and exposed cultures to siRNA targeting \u003cem\u003eREST\u003c/em\u003e (siREST) or scrambled siRNA throughout the activation period. qPCR and immunofluorescence showed significant \u003cem\u003eREST\u003c/em\u003e knock-down (KD) by siREST (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea, b). Vitamin A-specific autofluorescence is lost upon HSC activation\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e, and phHSCs had significantly higher cellular autofluorescence following \u003cem\u003eREST\u003c/em\u003e KD as compared with exposure to scrambled siRNA (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec), as measured by flow cytometry. Furthermore, \u003cem\u003eREST\u003c/em\u003e KD strongly upregulated the transcript levels of synaptophysin (\u003cem\u003eSYP\u003c/em\u003e), an established HSC marker\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e and direct target of REST\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e, and significantly downregulated pro-fibrogenic collagen 1α1 (\u003cem\u003eCOL1A1\u003c/em\u003e) and the activated HSC marker α smooth muscle actin (\u003cem\u003eACTA2\u003c/em\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed). Similarly, \u003cem\u003eREST\u003c/em\u003e KD in the human hepatic stellate cell line LX2\u003csup\u003e26\u003c/sup\u003e (Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003ea,b) reduced \u003cem\u003eCOL1A1\u003c/em\u003e and elevated \u003cem\u003eSYP\u003c/em\u003e (Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003ec). Further, both \u003cem\u003eREST\u003c/em\u003e KD and inhibition of activation using 72h serum starvation reduced transcript levels of \u003cem\u003eCOL1A1\u003c/em\u003e in LX2 cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ee). Of note, serum starvation did not further reduce \u003cem\u003eCOL1A1\u003c/em\u003e levels beyond \u003cem\u003eREST\u003c/em\u003e KD, indicating that serum starvation downregulated \u003cem\u003eCOL1A1\u003c/em\u003e by a mechanism that requires REST (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ee). Thus, \u003cem\u003eREST\u003c/em\u003e KD reduced the mRNA levels of the key pro-fibrogenic gene \u003cem\u003eCOL1A1\u003c/em\u003e in activated HSCs.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe next investigated the role of REST in the short-term response of activated HSCs to re-exposure to pro-activation microenvironments. In activated phHSCs exposed to fresh FBS (10%), \u003cem\u003eREST KD\u003c/em\u003e promoted a significant upregulation of \u003cem\u003eACTA2\u003c/em\u003e and \u003cem\u003eSYP\u003c/em\u003e transcripts but caused no significant change in the level of \u003cem\u003eCOL1A1\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ef). Similarly, in cultures of activated LX2 cells, \u003cem\u003eREST\u003c/em\u003e KD did not abrogate the TGFβ-dependent induction of \u003cem\u003eCOL1A1\u003c/em\u003e. Of note, \u003cem\u003eREST\u003c/em\u003e KD reduced the relative level of \u003cem\u003eCOL1A1\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eg, Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003ed), corroborating REST-dependent regulation of \u003cem\u003eCOL1A1\u003c/em\u003e. Together, these data indicate that although \u003cem\u003eREST\u003c/em\u003e KD reduces \u003cem\u003eCOL1A1\u003c/em\u003e expression, re-exposure of activated HSCs to a pro-activation microenvironment can upregulate \u003cem\u003eCOL1A1\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eNext, we asked whether levels of COL1A1 and SYP protein are regulated by \u003cem\u003eREST\u003c/em\u003e in activated HSCs. \u003cem\u003eREST\u003c/em\u003e KD reduced secretion of COL1A1 from activated phHSCs in cell cultures from three out of four donors (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eh), suggesting that \u003cem\u003eREST\u003c/em\u003e promotes activated HSC-mediated fibrogenesis. Levels of SYP were higher in phHSCs and LX2 cells exposed to siRNA targeting \u003cem\u003eREST\u003c/em\u003e as compared with scrambled siRNA (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ei, S2e). Surprisingly, intracellular COL1A1 levels were increased after \u003cem\u003eREST\u003c/em\u003e KD in both phHSCs (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ei, Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003ef) and LX2s (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ej, k, Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003ee, g) as measured by confocal microscopy immunofluorescence and flow cytometry, respectively, suggesting that \u003cem\u003eREST\u003c/em\u003e KD regulates synthesis and secretion of COL1A1 independently. Together, these results indicate that \u003cem\u003eREST\u003c/em\u003e regulates the \u003cem\u003ein vitro\u003c/em\u003e pro-fibrogenic activity of activated HSCs.\u003c/p\u003e \u003cp\u003e \u003cb\u003eREST\u003c/b\u003e \u003cb\u003eregulated HSC proliferation\u003c/b\u003e\u003c/p\u003e \u003cp\u003eHSC survival and proliferation are key in the pathophysiology of MASH and activation of HSCs is known to induce proliferation\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. We found that cell numbers were significantly lower in cell cultures following \u003cem\u003eREST\u003c/em\u003e KD as compared with exposure to scrambled siRNA, both in phHSC and LX2 cell cultures during activation (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea, b). Accordingly, we hypothesized that \u003cem\u003eREST\u003c/em\u003e controls proliferation of activated HSCs. Activated phHSCs with and without \u003cem\u003eREST\u003c/em\u003e KD were studied using RNA-sequencing and gene set enrichment analysis (GSEA). Proliferation-related GO terms were selected from the identified pool of significantly regulated gene sets. We observed that pathways linked to proliferation were downregulated in phHSCs after \u003cem\u003eREST\u003c/em\u003e KD (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec). To gain more insight, we asked how \u003cem\u003eREST\u003c/em\u003e KD affected mRNA levels of genes included in the gene set with the lowest normalized enrichment score among these pathways, i.e. GO:0006261 \u0026ldquo;DNA-templated DNA replication\u0026rdquo;. Analysis across four independent donor cultures showed that \u003cem\u003eREST\u003c/em\u003e KD consistently reduced transcript levels across the GO:0006261 gene set (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed). Further, the top 10 most differentially expressed transcripts from the GO sets related to proliferation (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec) were similarly downregulated following \u003cem\u003eREST\u003c/em\u003e KD (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ee).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAccordingly, we proceeded to investigate whether \u003cem\u003eREST\u003c/em\u003e KD controls HSC proliferation. To test this, we used an IncuCyte\u0026reg; live cell imaging system to measure HSC growth with and without concomitant serum starvation. We knocked down \u003cem\u003eREST\u003c/em\u003e in phHSCs during activation for nine days and subsequently cultured the cells in the IncuCyte\u0026reg; live cell imaging system for an additional three days in media supplemented with 10% or 0% FBS. Subsequent analysis revealed that \u003cem\u003eREST\u003c/em\u003e KD reduced the confluence of activated HSC to a similar extent as did serum starvation (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ef-h). Further, \u003cem\u003eREST\u003c/em\u003e KD LX2 cells had significantly higher levels of CellTrace fluorescence intensity after 96h of culture as compared with LX2 cells exposed to scrambled siRNA (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ei), indicating reduced cell division. Together, these results show that REST regulates proliferation of activated HSCs.\u003c/p\u003e \u003cp\u003e \u003cb\u003eREST\u003c/b\u003e \u003cb\u003eregulated HSC survival\u003c/b\u003e\u003c/p\u003e \u003cp\u003eNext, we investigated \u003cem\u003eREST\u003c/em\u003e in the context of activated HSCs survival. \u003cem\u003eREST\u003c/em\u003e KD promoted phHSC death with an effect size similar to that of serum starvation (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea), and in LX2 cells to an even higher extent than serum starvation (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb,c). To study mechanism, we next asked if \u003cem\u003eREST\u003c/em\u003e KD promotes HSC apoptosis. \u003cem\u003eREST\u003c/em\u003e KD increased caspase-3 cleavage in LX2 cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ed-f), and exposure of LX2 cells to the caspase inhibitor Z-VAD-FMK attenuated \u003cem\u003eREST\u003c/em\u003e KD-mediated cell death (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eg), identifying a role for apoptosis in \u003cem\u003eREST\u003c/em\u003e KD-driven cell death. In contrast, exposure of LX2 cultures to Z-VAD-FMK did not significantly impact cell growth (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eh) with or without \u003cem\u003eREST\u003c/em\u003e KD, indicating that inhibition of \u003cem\u003eREST\u003c/em\u003e KD-driven apoptosis is not sufficient to restore growth of activated HSCs, and that other mechanisms control REST-dependent proliferation.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eREST has previously been linked with PI3K\u003csup\u003e\u003cspan additionalcitationids=\"CR28 CR29\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e and mTOR\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e cascades in a context- and cell type-dependent manner\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. The PI3K/AKT/mTORC1 cascade is known to be activated by growth factors and to inhibit cell death, promote proliferation, and to constitute a critical component in HSC activation\u003csup\u003e\u003cspan additionalcitationids=\"CR34 CR35 CR36\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. We observed elevated AKT phosphorylation following \u003cem\u003eREST\u003c/em\u003e KD in LX2 cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ei), supporting a link between \u003cem\u003eREST\u003c/em\u003e and the PI3K signaling cascade in activated HSCs. To study the mechanism behind \u003cem\u003eREST\u003c/em\u003e-driven growth and survival of activated HSC, we exposed LX2 cells to the PI3K inhibitor LY294002. LY294002 exposure increased cell death by 59\u0026thinsp;\u0026plusmn;\u0026thinsp;2% in scrambled siRNA-transfected cells, and by 35\u0026thinsp;\u0026plusmn;\u0026thinsp;7% in \u003cem\u003eREST\u003c/em\u003e KD cultures (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ej,k), suggesting that at least part of the REST-driven regulation of LX2 survival requires PI3K cascade activity. In contrast, LY294002 exposure did not significantly suppress LX2 growth following \u003cem\u003eREST KD\u003c/em\u003e as measured in an IncuCyte\u0026reg; live cell imaging system (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003el,m). Further, inhibition of PI3K activity did not significantly affect REST-dependent regulation of either mRNA levels (Fig. S3a) or intracellular protein levels (Fig. S3b) of COL1A1, suggesting that REST-driven proliferation, survival and fibrogenesis are regulated by distinct downstream signaling mechanisms.\u003c/p\u003e \u003cp\u003eTo further interrogate \u003cem\u003eREST-\u003c/em\u003emediated regulation of the PI3K signaling cascade, we asked whether expression of the PI3K subunit p85α (PIK3R1), a key factor in HSC activation\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e, was regulated by \u003cem\u003eREST\u003c/em\u003e. \u003cem\u003ePIK3R1\u003c/em\u003e mRNA levels were significantly reduced by REST KD in phHSCs (Fig. S3c) and LX2 cells (Fig. S3d, e). Accordingly, we knocked down \u003cem\u003eREST\u003c/em\u003e, \u003cem\u003ePIK3R1\u003c/em\u003e, \u003cem\u003eRAPTOR\u003c/em\u003e (\u003cem\u003eRPTOR\u003c/em\u003e) or combination thereof in LX2 cells (Fig. S3f) and measured cell growth and cell death. Knock down of \u003cem\u003ePIK3R1\u003c/em\u003e or \u003cem\u003eRPTOR\u003c/em\u003e had no significant effect on survival of LX2 cells during serum starvation or \u003cem\u003eREST\u003c/em\u003e KD-mediated LX2 cell death (Fig. S3g). Further, inhibition of mTORC1 using rapamycin attenuated cell division in LX2 cells exposed to either siRNA targeting \u003cem\u003eREST\u003c/em\u003e or scrambled siRNA as measured by CellTrace fluorescence (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003en). Of note, the relative attenuation was significantly lower in \u003cem\u003eREST\u003c/em\u003e KD cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eo). Similarly, in \u003cem\u003eREST\u003c/em\u003e KD LX2 cells, there was no additional reduction of cell growth following knock down of \u003cem\u003ePIK3R1\u003c/em\u003e or \u003cem\u003eRPTOR\u003c/em\u003e (Fig. S3h), supporting the notion that PIK3R1/mTORC1 cascades and REST are linked in suppression of LX2 growth. Interestingly, \u003cem\u003ePIK3R1\u003c/em\u003e KD fully reversed downregulation of \u003cem\u003eCOL1A1\u003c/em\u003e expression following \u003cem\u003eREST\u003c/em\u003e KD (Fig. S3i), indicating that \u003cem\u003eREST\u003c/em\u003e KD-driven suppression of \u003cem\u003eCOL1A1\u003c/em\u003e expression was mediated in a PIK3R1-dependent manner. Together, the data suggests that \u003cem\u003eREST\u003c/em\u003e KD-dependent regulation of activated HSC fibrogenesis, growth and survival is partially mediated by the PI3K/mTORC1 cascade. PIK3R1 was required for \u003cem\u003eREST\u003c/em\u003e KD-driven \u003cem\u003eCOL1A1\u003c/em\u003e suppression, but \u003cem\u003eREST\u003c/em\u003e KD-driven death was not abrogated by PI3K/mTORC1 inhibition, further supporting the notion that REST regulates key aspects of HSC biology by discrete mechanisms.\u003c/p\u003e \u003cp\u003e \u003cb\u003eREST\u003c/b\u003e \u003cb\u003eregulates metabolism in activated HSCs\u003c/b\u003e\u003c/p\u003e \u003cp\u003eREST has been linked to metabolic regulation of neuronal function\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e,\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e and metabolic reprogramming is key for HSC activation\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Based on our observation that \u003cem\u003eREST\u003c/em\u003e KD exacerbated the effect of serum starvation on growth and promoted death in phHSCs and LX2 cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea, S4a), we proceeded to study \u003cem\u003eREST\u003c/em\u003e in HSC mitochondrial function. Since mitochondria are the key energy source driving HSC activation\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e, we asked whether \u003cem\u003eREST\u003c/em\u003e was involved in the regulation of mitochondrial metabolism in activated HSCs. Analysis of the oxygen consumption rate (OCR) revealed that the basal respiration was elevated in LX2 cells with \u003cem\u003eREST\u003c/em\u003e KD (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb-d). The ratio of mitochondrial to genomic DNA was higher in \u003cem\u003eREST\u003c/em\u003e KD (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ee) as was MitoTracker Deep Red FM fluorescence (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ef), indicating an increase in mitochondrial mass and mitochondrial numbers. ATP-linked respiration was unchanged while proton leakage was significantly higher in LX2 cells with \u003cem\u003eREST\u003c/em\u003e KD, observations that were further supported by the decrease in coupling efficiency and the increase in the spare respiratory capacity (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec, d). In line with these findings, JC-1 staining\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e indicated that the mitochondrial membrane potential was lower following \u003cem\u003eREST\u003c/em\u003e KD (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eg). Further, dichlorofluorescin diacetate (DCFDA) staining\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e indicated reduced ROS production in \u003cem\u003eREST\u003c/em\u003e KD LX2 cells as compared with LX2 cells exposed to scrambled siRNA (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eh). Together, the data links \u003cem\u003eREST\u003c/em\u003e with mitochondrial metabolism and growth in activated HSCs.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSince mitochondria-dependent ATP production is key for HSC activation\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e, we next asked whether \u003cem\u003eREST\u003c/em\u003e KD affected the energy balance. We observed elevated levels of AMPKα Thr172 phosphorylation following \u003cem\u003eREST\u003c/em\u003e KD (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ei), indicating an increased AMP/ATP ratio. Furthermore, LipidTOX red staining indicated that \u003cem\u003eREST\u003c/em\u003e KD elevated levels of intracellular lipids in phHSCs (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ej, Fig. S4b) and LX2s (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ek). These observations are congruent with insufficient quantities of ATP for activation and reduced lipid utilization in \u003cem\u003eREST\u003c/em\u003e KD HSCs. Of note, inhibition of PI3K by LY294002 exposure did not significantly affect \u003cem\u003eREST\u003c/em\u003e KD-driven lipid accumulation in LX2 cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003el), indicating that \u003cem\u003eREST\u003c/em\u003e-mediated regulation of lipid metabolism in activated HSCs is independent of PI3K activity.\u003c/p\u003e \u003cp\u003eAutophagy can act as an energy source during starvation, and \u003cem\u003eREST\u003c/em\u003e protects neurons from death in part through regulation of autophagy\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. Accordingly, we asked whether autophagy in HSC is affected by \u003cem\u003eREST\u003c/em\u003e KD. Staining of phHSCs with monodansylcadaverine (MDC), a marker for autophagic vacuoles, showed a significant increase in MDC fluorescence intensity following phHSCs \u003cem\u003eREST\u003c/em\u003e KD (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003em), indicating that \u003cem\u003eREST\u003c/em\u003e KD promoted autophagy. Further, \u003cem\u003eREST\u003c/em\u003e KD promoted LC3a/b cleavage in LX2 cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003en), although no accumulation of MDC-stained autophagic vacuoles was observed in LX2 \u003cem\u003eREST\u003c/em\u003e KD (Fig. S4c). Since AKT/mTORC1 axis was reported to suppress autophagy by phosphorylation Ser757 of ULK1\u003csup\u003e43\u003c/sup\u003e and AMPK is known to induce autophagy and oppose AKT-driven inhibition of autophagy\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e, we next studied the effect of \u003cem\u003eREST\u003c/em\u003e KD in this context. We observed that \u003cem\u003eREST\u003c/em\u003e KD reduced phosphorylation of Ser757 of ULK1 in LX2 cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003en), suggesting that AMPK activation in \u003cem\u003eREST\u003c/em\u003e KD promoted autophagy and attenuated AKT/mTORC1-driven suppression of autophagy (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Together, the data indicates that \u003cem\u003eREST\u003c/em\u003e KD disrupted mitochondrial metabolism in activated HSCs, stimulating autophagy, and suppressing activation.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eHere, we describe an essential role of REST in promoting activation of HSCs and sustaining their survival and growth. By analyzing scRNA sequencing data in mouse models of MASH, we found that the level of \u003cem\u003eREST\u003c/em\u003e was linked with disease activity. \u003cem\u003eIn vitro\u003c/em\u003e experiments showed that REST regulates HSC activation, survival and proliferation, key processes in the pathophysiology of MASH and hepatocellular carcinoma.\u003c/p\u003e \u003cp\u003eThe cellular function of REST is known to change with its expression level\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Accordingly, the observation here that \u003cem\u003eREST\u003c/em\u003e was reduced in HSCs in multiple models of MASH is interesting: \u003cem\u003eREST\u003c/em\u003e is downregulated during the differentiation of neurons\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e and is critical for maintenance of neuronal function and health. Further downregulation of \u003cem\u003eREST\u003c/em\u003e in neurons impairs function, promotes cell death and reduces lifespan\u003csup\u003e\u003cspan additionalcitationids=\"CR18 CR19\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Similarly, we found that \u003cem\u003eREST\u003c/em\u003e expression in HSCs was reduced during activation. REST regulated key aspects of HSC activation, and further knock-down of REST negatively impacted activated HSC proliferation and survival.\u003c/p\u003e \u003cp\u003eIt is well established that collagen synthesis and release, cellular proliferation, and survival are key functions of HSC in the pathophysiology of liver fibrosis\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. A plethora of extracellular signals promote various aspects of HSC activation through different mechanisms. For instance, TGFβ is a key pro-fibrogenic stimulus and growth factors such as PDGFβ are essential drivers of HSC proliferation\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. The findings here indicate that REST functionally connects aspects of HSC activation as evidenced by the observations that \u003cem\u003eREST\u003c/em\u003e KD reduced COLA1A expression and secretion, reduced proliferation, and promoted apoptosis. Paradoxically, although \u003cem\u003eREST\u003c/em\u003e KD downregulated COL1A1 expression and secretion, intracellular levels of COL1A1 protein were elevated. This discrepancy could be explained by independent regulation of transcription, translation and protein secretion by REST. We speculate that reduced secretion of COL1A1 protein following \u003cem\u003eREST\u003c/em\u003e KD could be the cause of accumulation of COL1A1 protein in intracellular depots. This accumulation may lead to ER stress and contribute to the elevated cell death we observed following \u003cem\u003eREST\u003c/em\u003e KD.\u003c/p\u003e \u003cp\u003eInterestingly, REST-dependent signaling cascades that regulate COL1A1 production, and survival and proliferation of activated HSC, were not interdependent. For instance, REST-driven survival depended on activity of the PI3K signaling cascade, but REST and PI3K independently regulated growth. It is, however, apparent that other mechanisms induced by REST KD opposed the effects of PI3K signaling, as both autophagy and cell death were promoted by REST KD while proliferation was impaired. First, PI3K is known to promote survival by opposing apoptosis\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e, but in REST KD we observed both increased AKT phosphorylation and apoptosis, indicating simultaneous induction of PI3K signaling and apoptosis. Accordingly, we hypothesize that REST promotes apoptosis in part through mechanisms that are not targeted by the PI3K cascade. Second, activation of the mTORC1 cascade was observed to inhibit autophagy\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e, and activation of the PI3K/AKT cascade promoted mTORC1 activity\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e, but we found simultaneous induction of PI3K activity and autophagy following REST KD. It is likely that REST KD shields the autophagic machinery from PI3K-driven inhibition by inducing AMPK activity. AMPK stimulates growth factor-driven activation of PI3K and AKT but diminishes AKT-driven activation of mTORC1 and opposes mTORC1-driven inhibition of autophagy\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e,\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. We observed higher AMPK phosphorylation following \u003cem\u003eREST\u003c/em\u003e KD, suggesting that change in energy metabolism balance after \u003cem\u003eREST\u003c/em\u003e KD stimulates the PI3K/AKT cascade by inducing AMPK activity. It is possible that a similar mechanism inhibits anti-apoptotic activity of the PI3K/AKT cascade following \u003cem\u003eREST\u003c/em\u003e KD. Importantly, neither PI3K nor apoptosis inhibition abrogated the reduction in cell growth following \u003cem\u003eREST\u003c/em\u003e KD, indicating that REST regulates growth and death of activated HSCs through independent mechanisms.\u003c/p\u003e \u003cp\u003eHSC activation involves profound changes in cell metabolism and these changes drive HSC activation\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. ATP production through β-oxidation and mitochondrial respiration is a critical energy source for activated HSCs. Interestingly, loss of \u003cem\u003eREST\u003c/em\u003e induced proton leakage in mitochondria and reduced coupling efficiency. In line with this, we observed higher AMPK phosphorylation following \u003cem\u003eREST\u003c/em\u003e KD in LX2 cells. Further, we observed accumulation of lipids in activated HSCs following \u003cem\u003eREST\u003c/em\u003e KD, suggesting that lipid utilization during activation was impaired in the absence of REST. These changes in lipid utilization, possibly due to mitochondrial dysfunction or metabolic rerouting within the cells, could cause reduced ATP levels following \u003cem\u003eREST\u003c/em\u003e KD. Notably, we observed a similar accumulation of intracellular lipids following serum starvation. Serum drives activation of HSCs, causing activation-dependent utilization of lipids, and, accordingly, serum starvation abrogates this effect\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. In contrast to the \u003cem\u003eREST\u003c/em\u003e KD effects, we did not observe a reduction of mitochondrial membrane potential and elevated AMPK phosphorylation during serum starvation, suggesting that \u003cem\u003eREST\u003c/em\u003e KD and serum starvation reduce HSC activation through different mechanisms. Together, these observations indicate that REST regulates cellular metabolic pathways for ATP production necessary to sustain HSC activation.\u003c/p\u003e \u003cp\u003eIn summary, our study uncovers REST as a novel factor required for HSC activation, growth and survival. Inactivation of REST in activated HSCs disrupts mitochondrial metabolism, promotes apoptosis and reduces COL1A1 expression, indicating that REST is a key factor linking activation-driven changes in activated HSC metabolism, growth and survival. The findings identify REST in activated HSCs as a novel potential therapeutic target in treatment of MASH.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eCell culture\u003c/h2\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003ePrimary human hepatic stellate cells\u003c/h2\u003e \u003cp\u003ePrimary human hepatic stellate cells were purchased from LifeNet Health (USA), thawed, seeded at the density of 5000 cells/cm\u003csup\u003e2\u003c/sup\u003e, and cultured according to manufacturer\u0026rsquo;s instructions. Cells were transfected with scrambled siRNA (ThermoFisher, #4390843, Waltham, Massachusetts, USA) or siRNA targeting REST (Santa Cruz, #sc-38129, Dallas, Texas, USA) using reverse transfection with RNAiMAX (ThermoFisher, #13778-150). Complexes were prepared in Opti-MEM (ThermoFisher, #31985-047) and placed in culture plates. Cells were diluted in DMEM (ThermoFisher, #31966021) with GlutaMax and 20% FBS (ThermoFisher, #10270-106), placed into the wells containing complexes mixed. Cells were incubated in DMEM/Opti-MEM with GlutaMax and 10% FBS for 24h. Next, media was replaced with DMEM containing GlutaMax and 10% FBS. Cells were cultivated for 5 days, counted, passaged and re-transfected using the identical protocol.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e\n\u003ch3\u003eImmortalized human hepatic stellate cells (LX2)\u003c/h3\u003e\n\u003cp\u003eLX2 cells were maintained in DMEM with 10% FBS and seeded at the density of 21000 cells/cm\u003csup\u003e2\u003c/sup\u003e. Cells were transfected with scrambled siRNA (ThermoFisher, #4390843), siRNA targeting REST (Santa Cruz, #sc-38129), siRNA targeting PIK3R1 (ThermoFisher, #AM6708, assay ID 118085) or siRNA targeting RPTOR (ThermoFisher, #AM6708, assay ID 140660) using reverse transfection as described above. After 24h, media was replaced with DMEM containing GlutaMax and 10% FBS or DMEM with GlutaMax and without FBS for experiments involving serum starvation. For experiments involving TGFβ (Merck, #GF346, Germany), 24h after transfection media was replaced with DMEM containing GlutaMax without FBS. 24h later, media was replaced, and cells were exposed to 5 ng/mL of TGFβ. For experiments involving chemical inhibitors, cells were exposed to inhibitors 24h after transfection and incubated for 72h. For experiments involving rapamycin, cells were exposed to the inhibitor for 1h and media was replaced with fresh DMEM with 10% FBS. For all experiments, cells were harvested 96h after transfection.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eGeneration of a stable LX2 cell line with an activation reporter gene\u003c/h2\u003e \u003cp\u003eThe reporter construct indicating LX2 activation was a kind gift from Drs. Li and Xiang at the Peking University Health Science Center. Briefly, the COL1A1 promoter sequence was used to regulate the expression of the enhanced green fluorescent protein (EGFP) (pLVX-COL1A1-EGFP). The COL1A1 promoter was amplified, and the EF-1α promoter of plasmid pWPI was replaced with the COL1A1 promoter to obtain the recombinant plasmid pWPI-COL1A1. The internal ribosome entry site (IRES) sequence before the EGFP sequence of pWPI-COL1A1 was replaced with a Kozak sequence (GCCACCATGG) to obtain the recombinant plasmid pWPI-COL1A1-EGFP. The fragment consisting of the COL1A1 promoter, Kozak sequence, and EGFP was amplified, and replaced the CMV promoter and AcGFP1 on the pWVX-AcGFP1-N1 plasmid, resulting in the final recombinant plasmid pLVX-COL1A1-EGFP. Lentiviral vectors containing the LX2-activation reporter were packed in HEK293T cells using the pCMVdR8.91 and pMD2.G packaging plasmids according to standard protocols. LX2 cells were transduced using the lentiviral vectors overnight, and transduced cells were selected using 2.5 ug/mL puromycin for two days. For maintenance and subcluturing, cell culture media was supplemented with 0.5 ug/mL puromycin, which was withdrawn prior to experimentation.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eIncucyte\u003c/h3\u003e\n\u003cp\u003ePrimary human hepatic stellate cells were thawed, seeded, transfected and cultured as described above. At day 6 post seeding, cells were trypsinized, counted and seeded on 96-well plates at the density of 1600 cells/well in DMEM\u0026thinsp;+\u0026thinsp;GlutaMax\u0026thinsp;+\u0026thinsp;10% FBS. After 48h media was replaced with fresh DMEM\u0026thinsp;+\u0026thinsp;GlutaMax\u0026thinsp;+\u0026thinsp;10% FBS or DMEM\u0026thinsp;+\u0026thinsp;GlutaMax, and cells were placed in Incucyte (Sartorius, Germany).\u003c/p\u003e \u003cp\u003eLX2 cells were seeded in 96-well plate at the density of 3000 cells/cm\u003csup\u003e2\u003c/sup\u003e and transfected as described above. 24h after transfection, media was replaced with fresh DMEM\u0026thinsp;+\u0026thinsp;GlutaMax\u0026thinsp;+\u0026thinsp;10% FBS, and cells were placed in Incucyte (Sartorius). For experiments involving chemical inhibitors, inhibitors were added to the media 24h after transfection.\u003c/p\u003e \u003cp\u003eCells were photographed every 4h for 72h. Analysis of confluence was performed with Incucyte 2022B Rev2 software using phase channel, AI confluence segmentation. For area under the curve (AUC) calculation, confluence was normalized to confluence at time point 0 per well.\u003c/p\u003e\n\u003ch3\u003eRNA isolation, reverse transcription and quantitative polymerase chain reaction (RT-qPCR)\u003c/h3\u003e\n\u003cp\u003eRNA from primary human hepatic stellate cells was isolated using RNeasy Mini Kit (Qiagene, #74104, Germany). RNA from LX2 cells was isolated using QIAzole (Qiagene, #79306). DNA digestion was performed using RNase-Free DNase (Qiagene, #79254). RNA concentration was measured using NanoDrop 2000 (ThermoFisher). 0.1 to 1 \u0026micro;g of RNA was used for RT. RT was performed using High-Capacity cDNA Reverse Transcription Kit (ThermoFisher, #4368814). qPCR was performed in QuantStudio 7 Pro (ThermoFisher) using GoTaq Probe qPCR kit (Promega, #A6102, Madison, Wisconsin, USA) for Taqman qPCR or SsoAdvanced Universal SYBR Green Supermix (Bio-Rad, #1725274, Hercules, California, USA) for SYBR Green qPCR and primers listed in Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e. mRNA levels were calculated as 2\u003csup\u003eCt(control)\u0026minus;Ct(sample)\u003c/sup\u003e and normalized to geometric mean of housekeeping genes mRNA level (\u003cem\u003eRPLP0\u003c/em\u003e, \u003cem\u003ePPIA\u003c/em\u003e, \u003cem\u003eSDHA\u003c/em\u003e).\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eSeaHorse extracellular flux analysis\u003c/h2\u003e \u003cp\u003eMetabolic analysis was performed using the Seahorse XF964 Flux Analyzer (Agilent, Santa Clara, CA, U.S.). LX2 cells were transfected with scrambled siRNA or siRNA targeting REST as described. 24h before SeaHorse analysis LX2 cells were re-seeded in 96-well XF Cell Culture Microplates (40000 cells/well) in complete DMEM medium with 10% FBS and GlutaMax. 1h prior to the analysis the medium was replaced with XF medium containing 5,5 mM glucose, 2 mM GlutaMax (ThermoFisher), 1 mM sodium pyruvate. Cells were incubated at 37\u0026deg;C without CO2 for 1h prior to the experiment. The oxygen consumption rate (OCR) was recorded at basal level and following injection of oligomycin (1 uM final), carbonyl-cyanide 4-trifluoromethoxy-phenylhydrazone FCCP (0,5\u0026thinsp;+\u0026thinsp;0,5 \u0026micro;M), and mixture of rotenone and antimycin A (5 \u0026micro;M). Running template was 2\u0026acute; mix and 4,5\u0026acute; measure. All chemicals were purchased from Sigma-Aldrich. Data were normalized on the number of cells per well. Normalization for cell number was carried out staining the nuclei with Hoescht 33342 (Molecular Probes, Eugine, Oregon, USA) for 10\u0026acute; and then imaging each well using BD pathway 855 (BD Biosciences, Franklin Lakes, U.S.) with 10x objective and montage 5x4. Cell number was counted with Cell profiler software.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eWestern blotting \u0026amp; ELISA\u003c/h2\u003e \u003cp\u003eLX2 cells were lysed in RIPA buffer with protease/phosphatase inhibitors (ThermoFisher, #1861281) (10 min, +4C). Protein concentration was determined using DC protein assay (Bio-Rad, #5000111) or Bredford assay (Bio-Rad, #500\u0026thinsp;\u0026minus;\u0026thinsp;0205). Proteins were segregated in 8\u0026ndash;15% polyacrylamide gels and transferred onto PVDF membranes using wet transfer. Protein loading was determined using Ponceau Red S staining. Membranes were blocked with 5% blocking reagent (Cell Signaling, #9999S, Denver, Massachusetts, USA) or 5% BSA diluted in PBS/0.05% Tween20, stained with primary (Table S3) and secondary (Table S4) antibodies, and secondary antibody fluorescence was measured using ChemiDoc (Bio-Rad).\u003c/p\u003e \u003cp\u003eCOL1A1 concentration in media was measured using ELISA (R\u0026amp;D Systems, #DY6220-05) according to manufacturers instructions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eFlow cytometry\u003c/h2\u003e \u003cp\u003ePrimary HSC were cultured as described above. Following trypsinization, cells were washed in PBS -/- with 10% FBS. Prior to antibody staining, cells were incubated in the blocking buffer containing PBS -/- and 10% FBS. Cells were stained using primary conjugated antibodies (Table S5) in (a) PBS -/-supplemented with 10% FBS, (b) LipidTox Red (ThermoFisher, #H34476) and 1 \u0026micro;M monodansylcadaverine (Sigma, #D4008) or (c) 50 nM MitoTracker Deep Red FM (ThermoFisher, #M22426) and 10 \u0026micro;g/mL JC-1 (ThermoFisher, #T3168) in PBS -/- for 30 minutes at 4C (a) or room temperature (b,c). Viability was assessed using Zombie Yellow\u0026trade; Fixable Viability Kit (BioLegend, #423102), 3 \u0026micro;g/mL propidium iodide (ThermoFisher, #P3566) or 1 \u0026micro;g/mL DAPI. For staining with antibodies against intracellular proteins, cells were fixed and permeabilized according to manufacturer protocol using BD Cytofix/Cytoperm\u0026trade; Fixation/Permeabilization Kit (BD, #554714). For detection of ROS, cells were stained with 2\u0026rsquo;,7\u0026rsquo;-dichlorofluorescin diacetate (Millipore, #287810, Burlington, Massachusetts, USA) for 30 min. at +\u0026thinsp;37C, washed with HBSS and fixed with 10% methanol/HBSS for 5 min. at RT. The gating strategies for flow cytometry are shown in Figures S5, S6, S7. Stained cells were analyzed using the Cytek Aurora 5 laser system (Cytek) and FlowJo software v.10 (FlowJo).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eImmunocytochemistry and confocal microscopy\u003c/h2\u003e \u003cp\u003ePrimary HSCs (4250 cells/cm\u003csup\u003e2\u003c/sup\u003e) and LX2 cells (25000 cells/cm\u003csup\u003e2\u003c/sup\u003e) were cultured on Ibidi 18-well \u0026micro;-Slide Cells were transfected with siRNA as described above. Cells were fixed in 2% PFA/PBS, blocked with 1% BSA in 0.1% Tween-20/PBS and stained with antibodies (Table S3) overnight at +\u0026thinsp;4C. Cells were washed with PBS and stained with secondary antibodies for 1h at +\u0026thinsp;4C. Next, cells were washed with PBS, stained with DAPI (1 ug/mL) and maintained in 10% glycerol/PBS at +\u0026thinsp;4C.\u003c/p\u003e \u003cp\u003eImages were acquired with a confocal laser scanning microscope (Nikon, ECLIPSE Ti2) equipped with 10x (N/A\u0026thinsp;=\u0026thinsp;0.45) and 20x (N/A\u0026thinsp;=\u0026thinsp;0.75) objectives and NIS-Elements Software (version 5.11.01). For quantification of COL1A1 and αSMA immunofluorescence, three to five fields of view (10x objective, 1269.76 x 1269.76 \u0026micro;m) were acquired per donor per condition using the same settings. Images were analyzed using QuPath software (version 0.5.1). Cell area was determined by the area above a set threshold. Total fluorescent signals of each cell were calculated by the mean fluorescent intensity of the cell times the area.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eCellTrace\u003c/h2\u003e \u003cp\u003eCellTrace\u0026trade; cell proliferation assay (ThermoFisher, #C34564) was performed according to the manufacturer\u0026rsquo;s instructions. Briefly, following trypsinization, LX2 cells were resuspended in the CellTrace working solution (1 \u0026micro;M). Cells were incubated for 20 minutes at 37\u0026deg;C. The reaction was quenched by DMEM with 2% FBS for 5 minutes at RT. Next, 4x10\u003csup\u003e4\u003c/sup\u003e cells per well were plated in a 24-well plate and transfected with siRNA as described above. After 24 h media was changed to DMEM\u0026thinsp;+\u0026thinsp;GlutaMax with 10% FBS. 96 hours after plating cells were trypsinised and fixed with 1% PFA. CellTrace accumulation was measured using Cytek Northern Lights flow cytometer (Cytek Biosciences, #NL-3000, Fremont, California, USA) and analyzed using FlowJo software version (10.10.0).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eFluorimetric Live/Dead assay\u003c/h2\u003e \u003cp\u003eFluorimetric measurement of cell death was performed using Cell Viability Assay Kit (Abcam, #ab112121, UK) according to manufacturers instructions. Briefly, green and red fluorescent dyes were diluted in PBS. Media was removed and LX2 cells were loaded with dyes for 1h at +\u0026thinsp;37C. Fluorescence (490\u0026thinsp;\u0026plusmn;\u0026thinsp;9 nm/525\u0026thinsp;\u0026plusmn;\u0026thinsp;8 nm and 540\u0026thinsp;\u0026plusmn;\u0026thinsp;12 nm/620\u0026thinsp;\u0026plusmn;\u0026thinsp;20 nm) was measured at well bottom with spiral averaging using VANTAstar fluorimeter (BMG Labtech, Germany). Dead cell-derived fluorescence was normalized with green fluorescence and presented relative to control samples as arbitrary units (AU).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eMitochondrial/Genomic DNA ratio\u003c/h2\u003e \u003cp\u003eTotal DNA was extracted using Genomic DNA GeneJET Purification Kit (ThermoFisher, #K0721). Mitochondrial DNA was quantified using primers for tRNA-Leu (5-CACCCAAGAACAGGGTTTGT-3, 5-TGGCCATGGGTATGTTGTTA-3)\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e, genomic DNA was quantified using primers for GAPDH pseudogene (5- AAGGGCATCCTGGGCTAC-3, 5-GTGGAGGAGTGGGTGTCG-3) and SsoAdvanced Universal SYBR Green Supermix (Bio-Rad, #1725274).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eSingle cell RNA sequencing analysis\u003c/h2\u003e \u003cp\u003ePublicly available data from single cell RNA sequencing datasets featuring primary mouse HSCs (datasets GSE17190419\u003csup\u003e21\u003c/sup\u003e, GSE206409\u003csup\u003e5\u003c/sup\u003e and Rosenthal et al., 2021\u003csup\u003e22\u003c/sup\u003e) were analyzed using R/Bioconductor package Seurat\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e. Gene expression analysis was performed using Seurat function FindMarkers. Gene Ontology and Gene Set Enrichment analysis was performed using R package clusterProfiler\u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e. High dimension weighted gene co-expression network analysis (hdWGCNA) was performed using the R package hdWGCNA\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eRNA-sequencing\u003c/h2\u003e \u003cp\u003ePrimary human HSCs were activated by plating for 12 days in DMEM containing 10% FBS and transfected with scrambled siRNA or siRNA targeting REST. Cells were exposed to fresh DMEM with 10% FBS 24h before RNA extraction. RNA was extracted using RNeasy Mini Kit (Qiagene, #74104) with DNA digestion (Qiagene, #79254). Pair-end mRNA sequencing of poly-A enriched RNA with the depth of 90\u0026nbsp;million reads was performed at Novogene. Alignment to human transcriptome (release hg38) and quantification was performed with Salmon\u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e. Data was imported\u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e and analyzed with DESeq2\u003csup\u003e54\u003c/sup\u003e. Heatmaps were plotted using pheatmap. Pathway analysis was performed with clusterProfiler\u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eStatistics and reproducibility\u003c/h2\u003e \u003cp\u003eStatistical analysis was performed using the R package ggpubr. Normality testing was performed using the Shapiro test. Pair-wise comparison was performed using \u003cem\u003et\u003c/em\u003e test or Mann-Whitney \u003cem\u003eU\u003c/em\u003e test followed by multiple comparison correction using Hommel test or FDR test. Statistical tests are indicated in figure legends. Interquartile range method was used for outlier detection. Outliers are shown as blue dots. P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered significant.\u003c/p\u003e \u003cp\u003e \u003cb\u003eAcknowledgements \u0026amp; Funding\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe reporter construct pLVX-COL1A1-EGFP was a kind gift from Dr. Li and Dr. Xiang at the Peking University Health Science Center. We thank Dr. Alda Saldan (Biomedicum FACS Facility, Karolinska Institutet) for the aid with experiments evaluating mitochondrial membrane potential. PSO is supported by the Swedish Research Council (Grant #2020\u0026ndash;04443, #2024\u0026ndash;03735), Heart-Lung Foundation (Grant #20200827, #20210524, #20230006), Karolinska Institutet (Grant #2023\u0026thinsp;\u0026minus;\u0026thinsp;01461, and #FoUI-987284). PSG is supported by \u0026Aring;ke Wibergs Stiftelse (Grant #M24-0065), the European Atherosclerosis Society, KI-fonder (Grant #2024\u0026ndash;02617), and Karolinska Institutet Network Medicine Alliance (Grant #2-776/2024). OA was supported by Novo Nordisk post doctoral fellowship and Stiftelsen Professor Nanna Svartz fond (Grant #2023\u0026ndash;02).\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eConflict of Interest\u003c/h2\u003e \u003cp\u003ePeder S Olofsson is a shareholder of Emune AB. The other authors have declared no financial or other conflicts of interest related to this submission.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eContributions\u003c/h2\u003e \u003cp\u003eVSS, OA and PSO conceived the project. VSS, WD, LT, NM, ASH, SH, FB, QG, MC, JJV performed experiments and analyzed the data. PSG created the pLVX-COL1A1-EGFP-expressing LX2 cells. VSS, PSO, LT, WD wrote the manuscript. All authors read, edited and approved the manuscript.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eEthics Approval\u003c/h2\u003e \u003cp\u003eThis study did not require ethical approval.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAvailability of Data and Materials\u003c/h2\u003e \u003cp\u003eThe data generated in this study (RNA-sequencing of phHSCs) has been deposited in the Gene Expression Omnibus (GEO) database with accession number GSE289433. The code will be made publicly available at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/ImmunoBioLab/Shavva2025\u003c/span\u003e\u003cspan address=\"https://github.com/ImmunoBioLab/Shavva2025\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e following publication.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eTsuchida, T. \u0026amp; Friedman, S. L. Mechanisms of hepatic stellate cell activation. Nat. Rev. Gastroenterol. Hepatol. 14, 397\u0026ndash;411 (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu, X., Xu, J., Brenner, D. A. \u0026amp; Kisseleva, T. Reversibility of Liver Fibrosis and Inactivation of Fibrogenic Myofibroblasts. Curr. Pathobiol. Rep. 1, 209\u0026ndash;214 (2013).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSimon, T. 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Genome Biol. 15, (2014).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-6237126/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6237126/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe activation of hepatic stellate cells (HSCs) is a key driver of liver fibrosis and inflammation in metabolic dysfunction-associated steatotic liver disease (MASLD). Targeting activated HSCs has shown promise in preventing liver fibrosis and cancer in mouse models. HSC activation is characterized by increased mitochondrial metabolism and upregulation of pro-fibrotic genes. Since RE1-silencing transcription factor (REST) is known to regulate cell fate, metabolism, and survival, we investigated its involvement in HSC activation. We observed reduced \u003cem\u003eRest\u003c/em\u003e mRNA levels in mouse activated HSCs as compared with mouse quiescent HSCs. In primary human HSCs and HSC-like LX2 cells, \u003cem\u003eREST\u003c/em\u003e knock-down led to decreased expression of pro-fibrogenic markers and was essential for the survival and proliferation of activated human HSCs \u003cem\u003ein vitro\u003c/em\u003e. \u003cem\u003eREST\u003c/em\u003e knock-down also promoted lipid accumulation, impaired mitochondrial metabolism, and increased AMPK phosphorylation and autophagy, resulting in reduced growth. Our findings identify a REST-dependent mechanism of HSC activation that is important for their survival and proliferation.\u003c/p\u003e","manuscriptTitle":"Survival and Proliferation of Activated Hepatic Stellate Cells Requires REST","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-20 11:57:14","doi":"10.21203/rs.3.rs-6237126/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"447b8761-b0a3-4f9e-8997-f6aaf15aea91","owner":[],"postedDate":"March 20th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":45957321,"name":"Biological sciences/Cell biology"},{"id":45957322,"name":"Health sciences/Diseases/Metabolic disorders"}],"tags":[],"updatedAt":"2026-01-30T15:36:38+00:00","versionOfRecord":{"articleIdentity":"rs-6237126","link":"https://doi.org/10.1186/s10020-025-01406-z","journal":{"identity":"molecular-medicine","isVorOnly":false,"title":"Molecular Medicine"},"publishedOn":"2026-01-28 00:00:00","publishedOnDateReadable":"January 28th, 2026"},"versionCreatedAt":"2025-03-20 11:57:14","video":"","vorDoi":"10.1186/s10020-025-01406-z","vorDoiUrl":"https://doi.org/10.1186/s10020-025-01406-z","workflowStages":[]},"version":"v1","identity":"rs-6237126","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6237126","identity":"rs-6237126","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}