Yaying Zhu#, Chengxiang Deng#, Bo Chen# 3, Jia He, Yanan Liu, Shan Lei, Weiju Lu, Cheng Peng*, Zhao Shan*

Yunnan Key Laboratory of Cell Metabolism and Diseases, Center for Life Sciences, School of Life Sciences, Yunnan University, Kunming, China, 650500

*For correspondence: chengpeng@ynu.edu.cn (CP); shanzhao@ynu.edu.cn (ZS)

Competing interest: The authors declare that no competing interests exist.

Acetaminophen (APAP) overdose is the leading cause of drug-induced acute liver failure worldwide, resulting in over 10,000 hospitalizations and approximately 500 deaths annually in the United States (Bernal and Wendon, 2013; Larson et al., 2005; Lee, 2007; Ostapowicz et al., 2002). The hepatotoxicity of APAP is initiated by its metabolic activation through cytochrome P450 enzymes, primarily in pericentral hepatocytes, generating the reactive metabolite N-acetyl-p-benzoquinone imine (NAPQI) (Dahlin et al., 1984; Hinson et al., 2004; Jack A. Hinson, 2010; Tujios and Fontana, 2011). NAPQI depletes glutathione and forms protein adducts, leading to oxidative stress and mitochondrial dysfunction that culminate in centrilobular necrosis (Dahlin et al., 1984; Hinson et al., 2004; Jack A. Hinson, 2010; Tujios and Fontana, 2011). Although the mechanisms of APAP bioactivation and toxicity are well-characterized, the early adaptive responses of hepatocytes—particularly their proliferative dynamics across lobular zones—remain poorly understood.

The liver is organized into functional hexagonal units termed lobules, where hepatocytes display spatial heterogeneity across three distinct zones: periportal (PP, zone 1), mid-zonal (Mid, zone 2), and pericentral (PC, zone 3) (Ben-Moshe et al., 2022a; Wang et al., 2024; Wu et al., 2024). These zones exhibit unique gene expression profiles and metabolic specializations(Halpern et al., 2017). Pericentral hepatocytes (zone 3), expressing high levels of cytochrome P450 enzymes, are the primary site of APAP-induced necrosis (Wang et al., 2024). However, recent spatial profiling, functional, and lineage tracing studies have identified midzonal hepatocytes (zone 2) as the predominant source of new hepatocytes during both homeostasis and repair, demonstrating significant proliferative capacity and metabolic plasticity (Chembazhi et al., 2021; He et al., 2021; Wei et al., 2021). Despite these advances, how mid-zone hepatocytes dynamically adapt during the initial phase of acute stress remains unknown.

A key regulator of cellular stress adaptation is the integrated stress response (ISR), which phosphorylates eukaryotic initiation factor 2α (eIF2α) to suppress global translation while selectively upregulating stress-responsive genes, including activating transcription factor 4 (Atf4) and its downstream target pro-apoptotic factor C/EBP homologous protein (Chop, encoded by DNA damage-inducible transcript 3 [Ddit3]) (Costa-Mattioli and Walter, 2020; Vasudevan et al., 2020). Although Chop is traditionally associated with apoptosis, its transcriptional targets exhibit significant overlap with those of Atf4, encompassing genes that paradoxically support cell survival and growth (Han et al., 2013; Marciniak et al., 2004; Uzi et al., 2013). These findings underscore the context-dependent duality of the Atf4-Chop axis across different stress conditions, highlighting the need to elucidate its precise regulatory roles in specific physiological settings.

Through spatial transcriptomics (ST) and functional analyses, we demonstrate that zonal heterogeneity in APAP metabolism drives a stress-adaptive response in mid-zone hepatocytes during early APAP-induced liver injury. This response is mediated by the Atf4-Chop-Btg2 axis, which orchestrates a transient proliferation arrest to prioritize cellular survival over regenerative capacity. Our findings elucidate the spatial and molecular mechanisms governing hepatocyte adaptation to acute injury, uncovering a critical survival-regeneration trade-off in early phase of acute liver injury.

To assess hepatocyte proliferation in early APAP-induced liver injury, wild-type C57BL/6J mice were intraperitoneally injected with 300 mg/kg APAP, and liver sections were analyzed at multiple time points (0, 3, 6, 12, and 24 hours post-APAP). Immunohistochemical staining for Ki67, a proliferation marker, revealed that mid-zone hepatocytes exhibited a roughly 3-fold higher basal proliferative activity than PP and PC hepatocytes under homeostatic conditions (0 hour) (Figure 1A, B), which in line with previous study (Wei et al., 2021). During the early injury phase (3–12 hours post-APAP), the number of Ki67+ hepatocytes in the mid-zone sharply declined from 70 to 20 per section, reaching proliferation levels similar to those in PP and PC zones (Figure 1A, B). By 24 hours post-APAP, mid-zone proliferation gradually recovered (Figure 1A, B), indicating a transient suppression of proliferation in this region during early APAP-induced liver injury. Further analysis of proliferation dynamics in each zone confirmed that all zones exhibited reduced proliferation during the injury initiation phase, with the mid-zone showing the most decline (Figure 1C). Comparative value change analysis (relative to 0 hour) further supported these findings, highlighting the mid-zone as the most affected region in terms of proliferative suppression during early APAP-induced liver injury (Figure 1D).

To investigate the mechanism underlying proliferation pause during early APAP-induced liver injury, liver sections from wild-type mice treated with APAP for various time points were subjected to ST using the Visium (10 × Genomics) platform (Figure S1A). Due to variability in mice’s responses to APAP, multiple mice were treated at each time point and one sample representing the average level at each time point was selected based on Hematoxylin and eosin (H&E) staining and serum alanine aminotransferase (ALT) levels for ST analysis. H&E staining of liver sections used for ST revealed a gradual increase in the necrotic area from 0 to 24 hours (Figure S1B). Under normal conditions (APAP 0h), each liver lobule was divided into three zones based on unbiased gene expression profiles. The PP zone was defined by enrichment of PP signature genes (e.g., Alb, Mup20, Cyp2f2, Pck1, Apoa4). The PC zone was defined by high expression of PC markers (e.g., Glul, Cyp2e1, Oat, Cyp1a2, Apoe). The Mid zone comprised regions with intermediate expression of PC and PP markers and elevated levels of Igfbp2 and Hamp (Figure 2A and S1C). Following APAP-induced injury (3h, 6h), the PC zone remained identifiable based on residual enrichment of PC signature genes (e.g., Cyp1a2, Glul) despite necrosis and reduced overall transcription, while PP gene expression remained largely unchanged. The Mid zone was defined as the transcriptional cluster between PC and PP regions exhibiting marked reprogramming, (e.g., Sqstm1, Igfbp1) (Figure 2A and S1C). Analysis of ST data at 0, 3, 6, and 24 hours post-APAP via UMAP plots consistently revealed distinct zonal clusters and dynamic spatiotemporal patterns during the early phase of acute injury (Figures S1D and S1E). Spatiotemporally resolved heatmaps of representative markers, such as Glul (a PC marker) and Cyp2f2 (a PP marker), further reinforced the stability of zonation profiles during early acute liver injury (Figure S1F).

We further analyzed differentially expressed genes (DEGs) in hepatocyte zones (PP, Mid, and PC) at 0, 3, 6, and 24 hours post-APAP (Figure 2A). Throughout the 0 to 24-hour period post-APAP, gene expression in the PP remained relatively stable. At 0, 3, and 6 hours post-APAP, genes associated with metabolism (such as Hsd17b13, Fabp1, Apoc2, Thrsp for fat metabolism; Rida, Hal, Gls2, Bhmt for fatty acid metabolism; Aldob, Pck1 for glucose metabolism) and transport (such as Alb, Hpx, Selenop, Trf, Lcn2) were prominently expressed, with a notable upregulation of inflammatory response-related genes (such as Orm1, Serpina1c, Lrg1) observed at 24 hours post-APAP (Figure 2A). In contrast, Mid-zone exhibited significant changes over time. Initially, it showed high expression of genes related to cell proliferation (such as Igfbp2), metabolism (such as Scd1, Cdo1, Ces1c, Ttr, Apoa1), and homeostasis (such as Hamp, Cps1, Rgn) at 0 hours, transitioning to stress response/cell survival (such as Hmox1, Hspa8, Atf3, Osgin1) and protein degradation/autophagy (such as Ubb, Ubc, Sqstm1) at 3 and 6 hours, and eventually shifting to cell proliferation and wound healing (such as Fgg, Fgl1, Fgb) by 24 hours post-APAP (Figure 2A). The PC showed a decrease in xenobiotic metabolism (such as Cyp3a11, Cyp2e1, Cyp1a2, Cyp2c37) and fat metabolism (such as Apoe, Akr1c6, Hpd, Oat) during early APAP-induced liver injury (at 0, 3, 6 hours post-APAP), with a marked increase in the expression of genes related to cytoskeleton (such as Actb, Krt8, Krt18) and redox regulation (such as Prdx1, Srxn1) observed at 24 hours post-APAP (Figure 2A). These differential expression patterns across various zones and time points post-APAP highlight significant changes in Mid-zone. Notably, our analysis suggests that the Mid-zone undergoes a stress adaption during the initial phases of acute liver injury.

Further analysis focuses on DEGs in Mid-zone at 6 hours post-APAP compared to 0 hour. The volcano plot revealed a notable increase in the number of genes significantly upregulated in the mid zones at 6 hours, predominantly associated with the stress response and adaptation (such as Atf3, Hmox1, Osgin1, Ddit3, Gadd45a, Atf4, Jun, Hspa1) as well as cell proliferation and apoptosis (such as Btg2, Egr1, Igfbp1, Sqstm1, Dnajb1) (Figure S1G). Among the significantly upregulated genes, Sqstm1 (Sequestosome 1) was selected for further validation. Sqstm1 encodes the p62 protein, a key regulator of autophagy and oxidative stress response (Huang et al., 2023). While Sqstm1 expression was almost evenly distributed across all zones at 0 hours, it was markedly elevated in the mid-zone at 3 and 6 hours post-APAP (Figure S1H). To validate and quantify our zonation approach during early injury, we compared the assigned zones with a classical division of the lobule into nine even layers spanning from the central vein (CV) to the portal vein (PV). Immunostaining and quantification for glutamine synthetase (GS, the protein product of Glul, a PC marker) and immunohistochemical staining for p62 (a midzone marker during early injury) and Cyp2f2 (a PP marker) further corroborated the zone definitions at each time point, demonstrating that our PC, Mid, and PP zones corresponded to layers 1–2, 3–6, and 7–9, respectively (Figure 2B–D). These findings suggest that while gene expression profiles change during acute injury, the fundamental spatial architecture of liver zones remains stable.

Further ST analysis revealed that Ki67-positive spots also show a transient reduction during the early injury (at 3 and 6 hours post-APAP) (Figure S1I), which aligns with the findings from Ki67 immunohistochemical staining. Cell cycle analysis is crucial for understanding cell proliferation, as it reveals the stages of the cell cycle that cells are in and how many cells are actively dividing (Woo et al., 2009). In the cell cycle, the S phase is responsible for DNA synthesis and replication, while the G2M phase prepares cells for and executes mitosis (Woo et al., 2009). Analysis of cell-cycle phase distribution, based on genes identified in previous studies (Bunch et al., 2023; Ohoka et al., 2005), revealed that Mid-zone hepatocytes exhibited a transient increase in S-phase genes at 3 hours, followed by a decline at later time points (6, and 24 hours). In contrast, Mid-zone hepatocytes exhibited reduced G2/M-phase scores at 3 and 6 hours, returning to baseline levels by 24 hours (Figure S1J). This suggests that Mid-zone hepatocytes may initiate DNA synthesis, but they are unable to complete the cycle due to the environmental toxicity or cellular damage. Nuclear Autoantigenic Sperm Protein (Nasp) binds to histones and facilitate chromatin assembly during the S phase (Richardson et al., 2006). Casein Kinase 1 Beta (Cks1b) participates in the control of G1 phase and the transition to the S phase (Peters et al., 2000). High Nasp levels indicate efforts toward DNA repair and chromatin stability, while low Cks1b levels point to a potential cell cycle arrest or a reduction in cell proliferation to manage the damage (Figure S1K). Overall, ST analysis revealed that Mid-zone hepatocytes exhibit distinct gene expression profiles during early acute injury.

Next, we aimed to elucidate the cause for the stress response observed in the Mid-zone during early APAP-induced liver injury. APAP is metabolized by cytochrome P450 enzymes (Cyp family) into reactive metabolites, particularly N-acetyl-p-benzoquinone imine (NAPQI), which can induce oxidative stress and damage cellular components if not promptly detoxified by glutathione (GSH) (Figure 3A). We analyzed the gene expression levels of Cyp family enzymes in hepatocyte zones (PP, Mid, and PC) at 0, 3, 6, and 24 hours post-APAP. Our analysis revealed a gradient decrease in the expression levels of most Cyp enzymes, showing a trend from high expression in PC to lower expression in PP (Figure 3B). Scatter plots of representative Cyp enzymes, such as Cyp2e1 and Cyp1a2, clearly showed a temporal and spatial expression pattern consistent with this gradient decrease from CV to PV (Figure S2A). Immunohistochemical staining of Cyp1a2 confirmed its gradient decrease in protein expression from the CV to the PV under homeostatic conditions (0 hour). However, following APAP-induced toxicity, Cyp1a2 expression declined around the CV—where necrosis was prominent—during the injury initiation phase (3–24 hours post-APAP). Consequently, the Mid-zone adjacent to necrotic regions emerged as the primary area retaining Cyp1a2 expression (Figure 3C, D). These results suggest that selective loss of Cyp- expressing pericentral hepatocytes leads to the mid- zone becoming the primary site of residual Cyp activity (Figure 3E).

To investigate this hypothesis, we administered mice with varying doses of APAP (100, 300, and 500 mg/kg) to induce different extents of liver injury. H&E staining revealed a dose-dependent increase in the size of the injured areas, with higher doses resulting in larger areas of injury (Figure S2B and S2C). We then performed immunofluorescence staining for Chop, the protein product of Ddit3, which was differentially upregulated in the Mid-zone at 6 hours post-APAP compared to 0 hours (as shown in Figure 2B), to assess the Mid-zone response. Our findings indicate that Chop expression is absent at low doses of APAP when necrosis does not occur. However, as the dose of APAP increases, the injured area expands, and Chop expression shifts toward the PV, consistently surrounding the injured area (Figure 3F and G). Together, these findings demonstrate that CV necrosis establishes the adjacent peri-necrotic region as the dominant site of APAP metabolism, contingent upon both Cyp family expression levels and APAP dosage. This spatially restricted metabolic adaptation drives subsequent region-specific transcriptional changes in the surrounding parenchyma (Figure 3H).

To explore master regulator of stress adaptation in Mid-zone, we constructed gene regulatory networks from the ST data. We identified the top 10 regulons in each zone at 3 and 6 hours post-APAP (Figure 4A and Figure S3A). A closer examination of the transcription factors (TFs) in the Mid-zone revealed that Atf4, Fos, Nfil3, Egr1, Maff, Atf3, and Ddit3 co-activated at both time points. Atf4, Chop, and Atf3 play crucial roles in managing cellular stress responses (Ohoka et al., 2005; Woo et al., 2009). Fos and Egr1 are immediate early genes that are rapidly activated in response to stimuli, regulating genes related to growth and stress responses (Bunch et al., 2023). Maff and Nfil3 contribute to the management of oxidative stress and metabolic processes (Lin et al., 2024; von Scheidt et al., 2021). The elevated activity of these TFs in the Mid-zone indicates that this region is undergoing a complex response to stress, serving as a critical area for cellular adaptation, survival, and potential necrosis during early liver injury.

Among the top 10 TFs with highest activity in the Mid-zone during early APAP-induced liver injury, Ddit3 and Atf3 were the two most highly expressed, with Atf4 ranking seventh (Figure 4B and Figure S3B). Since Ddit3 expression is often regulated by Atf4, and the Atf4-Ddit3 axis plays a critical role in stress adaptation (Zhang et al., 2022), we chose to focus on this pathway for further investigation. The violin plot analysis demonstrated that Atf4 and Ddit3 were prominently active in Mid-zone, particularly at 3 and 6 hours post-APAP (Figure 4C and Figure S3C). Gene regulatory networks illustrated associations between Atf4 and its target genes (such as Gdf15, Hspa8, and Ddit3) and Ddit3 with its targets (such as Hspa8, Hmox1, Osgin1, Gadd45a, and Egr1) at 3 hours post-APAP (Figure S3D). Heatmap further clearly showed that the target genes of Atf4 and Ddit3 are exclusively expressed in the Mid-zone hepatocytes at 6 hours post-APAP (Figure 4D). Many of these target genes were DEGs in Mid-zone during early APAP-induced liver injury, underscoring the critical role of the Atf4-Ddit3 axis in orchestrating gene expression changes in this region.

From the regulatory networks, it is evident that many genes regulated by Atf4 and Chop overlap. Therefore, we conducted a GO pathway analysis to identify the pathways co-regulated by Atf4 and Chop. The results revealed the

top 15 enriched pathways for genes co-regulated by Atf4 and Chop in Mid-zone at 3 and 6 hours post-APAP (Figure 4E and Figure S3E). These pathways encompass responses to unfolded proteins, stress, autophagy, and regulation of apoptotic signaling pathways. Additionally, GO pathway analysis identified the top 15 enriched pathways for up-regulated genes specifically in Mid-zone at 3 hours post-APAP compared to other zones, further highlighting pathways co-regulated by Atf4 and Chop in response to APAP (Figure S3F).

The pathways co-regulated by Atf4 and Chop point to the activation of ISR. To confirm the occurrence of ISR in Mid-zone during the initiation stage of APAP-induced liver injury, we performed immunohistochemical staining for phosphorylated eIF2α (p-eIF2α) at 0 and 3 hours post-APAP and observed increased expression of p-eIF2α in the mid-zone (Figure 4F). While the ISR is a generalized pathway integrating endoplasmic reticulum (ER) stress and other insults through eIF2α kinases, ER stress is a subset of cellular stresses that activates the unfolded protein response (UPR) via Perk/Ire1/Atf6. Their overlap occurs at Perk-eIF2α-Atf4, but ER stress uniquely engages Ire1/Xbp1 and Atf6 for ER-specific repair (Costa-Mattioli and Walter, 2020). To evaluate the involvement of ER stress in Mid-zone, we further analyzed the expression levels of genes related to stress response and ER stress based on DEGs identified in Mid-zone at 0, 3, and 6 hours post-APAP treatment (Figure 4G and Figure S3G). Most of stress response-related genes are highly upregulated (Figure 4G and Figure S3G). However, despite the high expression of Atf4 and Ddit3, other ER stress-related genes, such as Eif2ak3, Atf6, and Xbp1 (Zhang et al., 2022) either remained unchanged or were downregulated at 3 and 6 hours compared to 0 hour post-APAP (Figure 4G and Figure S3G). This analysis highlights the unique role of the Atf4-Ddit3 axis in Mid-zone during early APAP-induced liver injury.

To validate Mid-zone responses and transcriptional changes during early APAP-induced liver injury, we analyzed an additional scRNA-seq dataset from APAP-induced liver injury mice (Matchett et al., 2024). UMAP visualization demonstrated that hepatocytes could be clustered into three distinct zones (PP, PC, and Mid) at 3 and 6 hours post-APAP (Figures S4A and S4B). The zonal gene expression patterns were confirmed, with PC-specific genes (Glul, Cyp2e1, Cyp2a5), PP-specific genes (Alb, Cyp2f2, Sds), and Mid-zone genes (Hmox1, Atf3, Ubc) exhibiting distinct spatial distributions (Figure S4C). GO pathway analysis revealed the top 10 enriched pathways for upregulated genes in Mid-zonal hepatocytes at 3 and 6 hours post-APAP, highlighting key stress-response and cell death processes (Figures S4D and S4E). Consistent with our findings, SCENIC analysis also identified the activation of similar TF motifs across zonal regions, with distinct Ddit3 and Atf3 motifs activated in Mid-zone hepatocytes at 3 and 6 hours post-APAP (Figures S4F and S4G). These results reinforce the regulatory changes observed in our ST data, further emphasizing the unique role of mid-zonal hepatocytes in stress adaptation.

To investigate the role of Atf4-Ddit3 axis in cell fate determination, we first examined Atf4 expression in liver sections by immunohistochemical staining at 0 and 6 hours post-APAP. The results showed gradual increased expression of Atf4 in Mid-zone during early APAP-induced liver injury (Figure 5A). Subsequently, we employed the adeno-associated virus 8 (AAV8) and thyroxine-binding globulin (TBG) promoter method to overexpress Atf4 specifically in hepatocytes (Figure 5B). Atf4 overexpression significantly reduced APAP-induced liver injury, as evidenced by H&E staining and serum ALT levels (Figure 5C and 5D). Furthermore, immunohistochemical staining of Atf4 indicated its localization in hepatocyte nuclei around the injured area (Figure 5E). Immunofluorescence and immunohistochemical staining for Ki67 and p-eIF2α, respectively, showed that Atf4 overexpression notably decreased Ki67 expression and increased p-eIF2α expression (Figure 5F and 5G). Together, these findings suggest that elevated Atf4 expression enhances ISR, offering stress protection under conditions of reduced proliferation.

Atf4 often works with Ddit3 to mediate stress response, particularly in the context of the ISR (Kaspar et al., 2021; Ohoka et al., 2005; Woo et al., 2009). Given the low expression of Atf4 and high expression of Ddit3 in the Mid-zone, we performed Cut&Run to identify the genomic regions where Ddit3 binds during the initiation phases of APAP-induced liver injury. This approach was used to investigate how the Atf4-Ddit3 axis regulates the proliferation of Mid-zone hepatocytes. Compared with the input control, we observed heightened Ddit3-binding signals within the chromatin domains (Figure 6A). We further overlapped 6157 genes where Ddit3 binds, as identified by Cut&Run, with 47 DEGs specifically in the Mid-zone hepatocytes, identifying 39 overlapping genes termed Ddit3-binding DEGs (Figure 6B). Integrative Genomics Viewer (IGV) plots illustrate Ddit3-binding peaks and input controls at specific genomic sites such as Btg2, Atf3, Dnajb1, and Egr1 regulatory elements (Figure 6C).

Among the Ddit3-binding DEGs, we observed B-cell translocation gene 2 (Btg2), which is cell cycle inhibitor (Park et al., 2008; Stupfler et al., 2016; Yuniati et al., 2019). Moreover, Btg2 is one of the target genes that coregulated by Ddit3 and Atf4. Immunohistochemical staining of Btg2 in liver sections at 0 and 6 hours post-APAP revealed increased expression of Btg2 around the injured area in response to APAP (Figure 6D). Additionally, we investigated whether Ddit3 could upregulate Btg2 expression in Mid-zone. We overexpressed Ddit3 in the hepatocytes of wildtype C57BL/6J mice using the AAV8 method, with EGFP overexpression serving as a control (Figure 6E). Immunohistochemical staining of Btg2 in liver sections from mice overexpressing either AAV-TBG-EGFP or AAV-TBG-Ddit3 at 12 hours post-APAP indicated that Ddit3 overexpression significantly upregulated Btg2 expression post-APAP (Figure 6F). This result suggests that Btg2 is a downstream target of Ddit3.

To explore the role of Btg2 in liver injury and hepatocyte proliferation, we overexpressed Btg2 in hepatocytes of wild-type C57BL/6J mice (Figure 6G). H&E staining and serum ALT levels indicated that Btg2 overexpression significantly reduced APAP-induced liver injury (Figure 6H and 6I). Ki67 staining further demonstrated that Btg2 overexpression significantly decreased hepatocyte proliferation (Figure 6J). To assess potential confounds from AAV-based overexpression, we performed western blot analysis of Cyp2e1 protein levels in AAV-EGFP control mice and mice overexpressing Atf4 or Btg2 without APAP treatment. Compared with the AAV-EGFP group, Cyp2e1 expression was slightly decreased in the AAV-Atf4 and AAV-Btg2 groups (Figure S5A). Importantly, this reduction is well below the threshold of Cyp inhibition known to confer significant protection against APAP-induced liver injury (Ganetsky et al., 2019; Ye et al., 2022a). Therefore, the modest decrease in Cyp2e1 expression observed here is unlikely to account for the pronounced protective effects of Atf4 or Btg2 overexpression. We also quantified the penetrance of AAV-mediated overexpression. Mice were injected with 1.2 × 10¹¹ viral genome copies per animal, yielding average transduction efficiencies of 32%, 18%, and 23% for AAV-EGFP, AAV-Atf4, and AAV-Btg2, respectively (Figure S5B). Individual animal transduction efficiency showed a negative correlation with serum ALT levels (e.g. Pearson r = –0.7681, p = 0.0260 for Atf4; Figure S5C), demonstrating that greater transgene expression associates with stronger protection. Moreover, we used the CasRx-sgRNA system (Zhou et al., 2020) to knock down Btg2 by overexpressing either IP479-EFS-sgEGFP or IP479-EFS-sgBtg2 (Figure 6K). Knockdown of Btg2 using AAV8-CasRx achieved a moderate (~30%) reduction in Btg2 expression (Figure S5D). Despite this partial efficiency, we observed a significant increase in serum ALT levels (~2-fold) (Figure 6L), expansion of necrotic areas (~1.5-fold) (Figure 6M), and a marked increase in Ki67⁺ hepatocytes (~1.8-fold) compared to control mice (Figure 6N). Together, these findings illustrate Atf4-Ddit3 axis inhibits hepatocytes proliferation in Mid-zone via Btg2 during early APAP-induced liver injury.

To test whether the proposed mechanism applies to other liver injuries, we employed mouse models of partial hepatectomy (PHx) and carbon tetrachloride (CCl₄)-induced acute liver injury. CCl₄ causes centrilobular necrosis (similar to acetaminophen) but induces damage via free radical–mediated mechanisms rather than through cytochrome P450–dependent reactive metabolite formation (Wang et al., 2024). In our CCl₄ model (administered intraperitoneally in corn oil, with samples collected 18 h post-injection), the ISR was activated around injury sites, accompanied by decreased proliferation, as evidenced by increased expression of p-eIF2α, Atf4, Chop, and Btg2, along with reduced Ki67 expression (Figure S6A–G). Partial hepatectomy involves mechanical resection, resulting in ischemic injury in the remaining portion of the left lobe due to compromised blood supply (Qu et al., 2022). In this model (examined 24 h after surgery), ISR activation was similarly observed around ischemic injury sites, with increased expression of p-eIF2α, Atf4, Chop, and Btg2 and undetectable Ki67 expression (Figure S7A–G). Together, these findings suggest that the proposed mechanism may be broadly applicable to other types of liver injury.

Our study reveals that during the early phase of APAP-induced liver injury, mid-zone hepatocytes undergo a transient proliferation arrest, orchestrated by a distinctive transcriptional program centered on the Atf4-Chop axis. Spatial transcriptomics and functional assays demonstrate that this adaptive response promotes survival by upregulating the cell cycle inhibitor Btg2, highlighting a trade-off between cytoprotection and proliferative capacity. Notably, zonal metabolic heterogeneity in APAP processing drives preferential activation of these stress pathways in mid-zone hepatocytes. These findings uncover a critical mechanism by which hepatocytes prioritize stress adaptation before initiating regeneration, offering new insights into the spatiotemporal regulation of liver repair (Figure 7).

Our study reveals that mid-zone hepatocytes employ a protective proliferation arrest during early APAP-induced liver injury, mediated by the Atf4-Chop axis of the integrated stress response (ISR). While the ISR shares upstream initiators with ER stress (e.g., eIF2α phosphorylation), its outcomes are context-dependent. ER stress specifically activates the UPR through sensors inositol-requiring enzyme 1α (Ire1α), activating transcription factor 6 (Atf6), and protein kinase RNA-like ER kinase (Perk) (Wang et al., 2006), with genetic studies demonstrating their critical roles in APAP-induced liver injury knockout of Ire1α, X-box binding protein 1 (Xbp1), or Chop consistently attenuates liver damage, confirming the UPR’s contribution to hepatotoxicity (Hur et al., 2012; Nagy et al., 2007; Uzi et al., 2013; Ye et al., 2022b). Unlike classical ER stress, we find that mid-zone hepatocytes leverage the Atf4-Chop axis to transiently halt proliferation via Btg2, favoring survival. This aligns with emerging evidence that Chop can paradoxically support adaptation in mitochondrial stress (Kaspar et al., 2021; Kress et al., 2023), suggesting that its role in fine-turning of ISR in mammals.

Our findings on the early stress-induced proliferation arrest in mid-lobular hepatocytes (3–12h post-APAP) complement yet fundamentally extend prior knowledge of zonal liver regeneration. While foundational studies established perinecrotic hepatocytes as drivers of later proliferation (>24h) (Bajt et al., 2003), and recent work delineated CXCR2⁺ hepatocytes as pro-proliferative effectors at moderate doses (Nguyen et al., 2022) or CXCL14/p21-mediated senescence at high doses (Umbaugh et al., 2024), our work resolves an earlier, spatiotemporally distinct adaptive phase. Crucially, we identify mid-zone hepatocytes—not merely as transitional cells en route to proliferation—but as orchestrators of an active survival strategy via the Atf4-Chop-Btg2 axis, which redirects resources from division to cytoprotection (e.g., redox defense). This mechanism operates before necrosis expansion and is distinct from periportal/pericentral stress responses, which focus on 2h post-injury and lack mid-zone resolution (Umbaugh et al., 2021). By integrating spatial transcriptomics with functional validation (Cut&Run, AAV-Btg2 modulation), we establish causal links between stress signaling and quiescence—unlike descriptive profiling in earlier zonal studies (Wu et al., 2024). Thus, our model bridges early stress responses and later regeneration, revealing how mid-zone hepatocytes enable subsequent repair by prioritizing survival during the critical initiation window.

The antiproliferative activity of Btg2 has been studied through an integrated network of transcriptional and post-transcriptional cell cycle regulatory mechanisms. A study reveals that Btg2 functions as a molecular bridge between poly(A)-binding protein cytoplasmic 1 (Pabpc1) and the CCR4-NOT deadenylase complex subunit CNOT7 (Caf1), facilitating accelerated degradation of cell cycle-related transcripts through enhanced poly(A) tail removal (Stupfler et al., 2016). At the transcriptional level, Btg2 exerts specific control over G2/M progression by disrupting the positive feedback loop between cyclin B1 and its transcriptional activator Foxm1 (forkhead box protein M1), thereby suppressing cyclin B1 expression and inducing cell cycle arrest (Park et al., 2008). Furthermore, Btg2 demonstrates broader cell cycle inhibitory capacity through its interactions with cyclin-dependent kinases (Cdks) during G1/S transition and its ability to modulate the tumor suppressor p53 (TP53) (Yuniati et al., 2019). In the context of APAP-induced liver injury, the stress-responsive Atf4-Chop pathway induces Btg2 expression in mid-zonal hepatocytes, where it may coordinate a comprehensive proliferation arrest through these complementary mechanisms. This multilayered regulatory strategy enables hepatocytes to temporarily suspend cell division and redirect resources toward cellular repair and stress adaptation during the acute phase of toxic injury.

A key limitation of this study is the scarcity of early-phase APAP-induced liver injury human samples, which currently precludes definitive validation of the clinical relevance of these zonal adaptations. To address this gap, we performed a preliminary analysis of published ST data from APAP-overdose patients (Matchett et al., 2024). Intriguingly, in one of two analyzed patients, mid-zone hepatocytes exhibited transcriptional signatures remarkably consistent with our murine findings, including: (1) upregulation of Atf4-Chop pathways, and (2) downregulation of cell proliferation genes. While the small sample size (n=2) prevents definitive conclusions, these preliminary observations suggest potential conservation of stress-responsive mechanisms between murine and human mid-zone hepatocytes during APAP-induced liver injury. Further studies with larger clinical cohorts will be essential to verify these findings and establish their translational significance. While our ST analysis and Ki67 staining consistently indicated hepatocytes as the primary proliferating compartment during early APAP-induced liver injury, we recognize that non-parenchymal cells (e.g., immune cells, stellate cells) may contribute to division signals. Single-cell resolution techniques would be required to completely exclude these contributions, representing a technical limitation of bulk spatial transcriptomics. Additionally, our ST data alone do not independently confirm zonal cell cycle arrest: ST lacks overt separation in S/G2/M scores, uses non-canonical markers (e.g., Nasp, Cks1b), and has low sensitivity for rare Ki67⁺ events (~1% of spots). Thus, our primary evidence for midlobular proliferation dynamics comes from Ki67 immunohistochemistry (Figure 1), with ST findings (Figure 2E) shown as exploratory and directionally consistent with transient impairment, but not as definitive proof of cell cycle arrest.

In summary, our study uncovers a spatially coordinated stress adaptation mechanism in mid-zone hepatocytes during early APAP-induced liver injury, where the Atf4-Chop axis transiently suppresses proliferation via Btg2 upregulation. By elucidating how hepatocytes prioritize survival before initiating repair, our work provides a framework for understanding the dynamic interplay between stress adaptation and regeneration in tissues.

C57BL/6J mice (strain no. N000013) were used as wild-type (WT) mice. All mouse colonies were maintained at the Animal Core Facility of Yunnan University. The animal studies were approved by the Yunnan University Institutional Animal Care and Use Committee (IACUC, Approval No. YNU20220387). For APAP treatment, mice (8-12 weeks old) were fasted overnight (5:00pm to 9:00am) before i.p. injected with APAP (Sigma, A7085) at a dose of 300 mg/kg for male mice, as female mice are less susceptible to APAP-induced liver injury (Guerrero Munoz and Fearon, 1984). Male mice have been the choice in the vast majority of the studies of APAP-induced liver injury reported in the literature (Ben-Moshe et al., 2022b; Campion et al., 2008; Matchett et al., 2024; Shan et al., 2021). Therefore, we used male mice in the majority of the experiments presented. In one experiment, mice were i.p. injected with various doses of APAP (100, 300, 500mg/kg). For carbon tetrachloride (CCl4) treatment, mice were i.p. injected with corn oil or CCl4 (Macklin, Cat #C805329) at a dose of 0.8ul/g (corn oil dilute to 20%). For 70% partial hepatectomy, mice were placed in a supine position and secured under isoflurane inhalation anesthesia. A midline upper abdominal incision is made. The liver is gently exposed, and the left lateral lobe and the median lobe (comprising the left and right median lobes) are mobilized. The hepatic pedicle at the base of each lobe is ligated with 6-0 silk suture. After confirming the absence of bleeding, the lobes are excised just distal to the ligature, removing approximately 70% of the liver mass. The resection site is inspected for active bleeding. The abdominal incision is closed in layers with absorbable suture. Postoperatively, the mouse is placed on a heating pad for recovery.

In some experiments, APAP-treated mice were pre-injected intraperitoneally (i.p.) with either AAV-overexpression plasmid (AAV-TBG-Atf4/Ddit3/Btg2) or AAV-sgRNA knockdown plasmid (IP479-EFS-sgBtg2) for 3 weeks. AAV-TBG-EGFP and IP479-EFS-sgEGFP were used as controls, respectively. ALT measurement was performed using a diagnostic assay kit (Teco Diagnostics, Anaheim CA) to assess liver injury.

H&E staining Tissues were fixed overnight at 4°C using buffered 10% paraformaldehyde (Sangon Biotech, A500684-0500) and subsequently embedded in paraffin. For H&E staining, paraffin-embedded slides were deparaffinized and rehydrated following standard protocols. The sections were briefly immersed in hematoxylin for 30 seconds, rinsed in water, and then stained with Eosin solution for 3 minutes. After staining, the sections were dehydrated in ethanol, cleared in xylene, and mounted. Slides were examined under a microscope (Scan System, SQS-1000) and analyzed using ImageJ. Necrotic areas outlined by loss of cellular architecture on H&E.

Immunofluorescence on frozen section Immunofluorescence staining was conducted on frozen sections of fresh liver tissues. Initially, the tissues were fixed using 2% paraformaldehyde for 1 h and subsequently dehydrated overnight in a 30% sucrose solution. The following day, tissue embedding was performed using OCT (SAKURA, 4583), after which ultra-thin sections of 5μm were sliced. Permeabilization was achieved using 0.2% Triton X-100 for 10 minutes at room temperature, followed by blocking with 5% normal goat serum (VivaCell, C2530-0100). Primary antibodies against mouse GS (Proteintech, 11037-2-AP, 1:200), Chop (Proteintech, 15204-1-AP, 1:1000), Cyp1a2 (Proteintech, 15204-1-AP, 1:50) and Ki67 (Abcam, ab180569, 1:1000) were then applied. Secondary antibodies (Affinipure Goat Anti-Rabbit 488-conjugated, Jackson ImmunoResearch, 111-545-003, 1:400; Goat anti-Rabbit IgG(H+L) Cross Adsorbed Secondary Antibody,Alexa Fluor 568, Invitrogen, A11011, 1:1000 and Goat Anti-Mouse 594-conjugated, Jackson ImmunoResearch, 115-585-003, 1:200) were used accordingly. After washing, the slides were mounted using antifade medium, and nuclei were stained with DAPI (Beyotime, C1006, prediluted). Images were captured using an Olympus BP80 microscope and analyzed with ImageJ.

Immunohistochemistry Livers were fixed in 10% buffered formalin for 24 hours before paraffin embedding. For immunohistochemical staining of paraffin-embedded liver tissue, 5 μm sections were prepared, deparaffinized, and subjected to antigen retrieval by microwaving for 20 minutes. Antigen retrieval was performed in pH 6.0 sodium citrate buffer (Ki-67, P62, Chop, Cyp1a2, Cyp2f2), pH 6.4 sodium citrate buffer (p-eIF2A), or pH 9.0 Tris-EDTA buffer (Atf4, Btg2). Sections were blocked with 3% hydrogen peroxide and permeabilized with 0.2% Triton X-100 for 10 minutes at room temperature, followed by blocking with 5% normal goat serum (VivaCell, C2530-0100) for 1 hour at room temperature. Primary antibodies were incubated overnight at 4°C at the following dilutions in blocking buffer: Ki-67 (Abcam, ab15580, 1:400), P62 (Abclonal, A19700, 1:1000), Cyp2f2 (SantaCruz, sc-374540, 1:200), Chop (Proteintech, 15204-1-AP, 1:200), Cyp1a2 (Santa Cruz, sc-53241, 1:50), p-eIF2A (CST, 3398S, 1:50), Atf4 (CST, 11815S, 1:200), and Btg2 (Proteintech, 22339-1-AP, 1:100). Secondary antibodies used were Goat anti-Rabbit IgG (H+L) Secondary Antibody, Biotin (Invitrogen, 65-6140, 1:1000) and peroxidase-AffiniPure Goat Anti-mouse IgG(H+L) (Jackson ImmunoResearch, 115-035-003, 1:1000). Avidin, NeutrAvidin™, Horseradish Peroxidase conjugate (Invitrogen, A2664, 1:2000) was used as the third antibody. After three washes with PBS, color development was achieved using the DAB Peroxidase Substrate Kit (ZSGB-BIO, ZLI-9018), followed by quenching in distilled water. Slides were counterstained with hematoxylin (BBI, A600701-0050), dehydrated to xylene, and images were captured using an Olympus BP80 microscope or Scan System SQS-1000 and analyzed with ImageJ.

Mouse liver tissue (30 mg) was homogenized in 1 mL RIPA buffer (10 mM Tris–HCl pH 8.0, 1 mM EDTA, 0.5 mM EGTA, 1% Triton X-100, 0.1% sodium deoxycholate, 0.1% SDS, 140 mM NaCl, 1 mM PMSF, and protease inhibitor cocktail). The lysate was mixed with 5× SDS loading buffer (final 1×), boiled at 98°C for 10 min, and centrifuged at 12,000 rpm for 3 min at room temperature. Supernatants were separated by 10% SDS-PAGE, transferred to PVDF membranes, and blocked with 5% nonfat milk in TBST (Tris-buffered saline with 0.1% Tween-20). Membranes were incubated with primary antibodies against Cyp2e1 (Proteintech, 19937-1-AP, 1:5000) and GAPDH (Proteintech, 60004-1-Ig, 1:5000), followed by HRP-conjugated secondary antibodies: goat anti-mouse (Jackson ImmunoResearch, 115-035-003, 1:2000) and goat anti-rabbit (Jackson ImmunoResearch, 111-035-003, 1:2000). Protein bands were detected using enhanced chemiluminescence (ECL) and a Minichemi Chemiluminescence Imaging System.

Ground mouse liver tissue was lysed in TRIzol reagent (Invitrogen, 15596018) and centrifuged at 12,000 rpm for 10 min at 4°C. The supernatant was transferred to a new tube, mixed with 200 μL chloroform, incubated at room temperature for 10 min, and centrifuged. The aqueous phase was collected, mixed with an equal volume of isopropanol, incubated at 4°C for 10 min, and centrifuged. The supernatant was discarded, and the RNA pellet was washed twice with 75% ethanol, air-dried for 10 min, and dissolved in RNase-free water. RNA concentration and purity were measured using a NanoDrop spectrophotometer. Reverse transcription was performed using a cDNA first-strand synthesis kit (Takara, 6210B). Quantitative PCR was carried out in triplicates using PowerUp™ SYBR™ Green Master Mix (Thermo Fisher, A25742) on a QuantStudio 1 Real-Time PCR system (Life Technologies) according to the manufacturer’s instructions. Primer sequences are provided in Table S1.

AAV8 was produced using 293T cells cultured in one or more 175 cm² cell culture flasks. Cells were plated one day prior to transfection, reaching 50% confluence, which allowed them to reach 80-90% confluence the next day. For transfection of one 175 cm² flask, a mixture of 11.56 μg pHelper, 7.24 μg pAAV2/8 (Addgene, 112864), and 5.9 μg transgene plasmids was prepared in 3 ml Opti-MEM medium in a tube. PEI solution (1 μg/μl in water, pH 7.0, powder from Polysciences, 23966-2) was added to the tube at a ratio of 3 μg PEI per 1 μg DNA. The solution was mixed and incubated for 15 minutes before adding to the cell culture. At 24 hours post-transfection, the medium was replaced with antibiotic-containing medium (P+/S+) without FBS. At 72 hours after the medium change, cells were scraped off the flask, and 3 ml of chloroform was added, followed by vortexing for 5 minutes. Then, 7.6 ml of 5M NaCl was added and vortexed for 10 seconds. The solution was centrifuged at 3000g for 5 minutes at 4°C. The supernatant was transferred to a new 50 ml tube, and 9.4 ml of 50% (w/v) PEG 8000 was added, vortexed for 10 seconds, and left for 1 hour at 4°C. Subsequently, the mixture was centrifuged at 3000g for 30 minutes at 4°C. The supernatant was decanted, and the tube was inverted for 10 minutes to dry. Next, 1.4 ml of 50 mM HEPES buffer (pH 8.0) was added to re-suspend the pellet, and the solution was vortexed for 5 minutes. Then, 3.5 μl of 1M MgCl2, 2.8 μl of 5 μg/μl DNase I, and 2.8 μl of 5 μg/μl RNase A were added, and the mixture was incubated for 20 minutes at 37°C. The virus was then extracted with chloroform, and the solvent was replaced with PBS. AAV was concentrated by centrifugation at 14000g for 5 minutes at 4°C using a 100kDa ultrafiltration tube (Beyotime, FUF058). Finally, the samples were aliquoted and stored at -80°C after titer measurement.

After administering APAP, mice were euthanized at 0h, 3h, 6h and 24h time points using carbon dioxide. Freshly harvested liver tissues were frozen using isopentane and liquid nitrogen, embedded in OCT, sectioned into 10 μm slices, and mounted on Visium tissue optimization or spatial gene expression slides. Tissues were fixed with methanol, stained with H&E, and imaged at 40× magnification. Tissue permeabilization was optimized for 6 minutes. Libraries were prepared according to the Visium spatial gene expression user guide, with cDNA amplification cycles adjusted per time point, and index PCR performed for 6 cycles. Libraries were pooled, loaded at 400 pM, and sequenced on a NovaSeq 6000 SP100 sequencer.

The raw data underwent processing using spaceranger (version 1.1.0) to perform several analytical tasks. These included detecting tissue boundaries, aligning reads to the reference genome, generating feature-spot matrices, conducting clustering to identify spatially distinct groups of cells, and analyzing gene expression patterns. Additionally, spaceranger facilitated the spatial placement of spots within the context of the slide image, providing a comprehensive spatial transcriptomic analysis of the tissue sample.

For the gene-spot matrices generated by Spaceranger, we utilized Scanpy (version 1.9.3) for analysis (Patro et al., 2017). Initially, routine statistical analyses were conducted, including calculating the number of detected UMIs (nUMI) and genes (nGene) in each spot. Basic quality control (QC) measures were then applied to the data. Specifically, spots with extremely low UMI or gene counts (min_counts=2000 ， min_cell=10) were excluded using scanpy.pp.filter_cells function.

Mitochondrial and hemoglobin genes were filtered out to focus on relevant gene expression. Spot matrix was filtered out to keep only spots overlaying tissue sections. Following QC, data normalization was performed using the scanpy.pp.normalize_total function, and log-transformation was applied using the scanpy.pp.log1p function. Subsequently, we identified 2000 highly variable genes using the scanpy.pp.highly_variable_genes function based on their expression means and variances.

Principal component analysis (PCA) was then applied to reduce the dimensionality of the data, projecting the spots into a lower-dimensional space using the identified principal components (PCs). Using the corrected PC matrices, we conducted unsupervised clustering based on shared nearest neighbors (SNN) and visualized the data using UMAP (Uniform Manifold Approximation and Projection) for further exploration and analysis.(Lim and Qiu, 2023)

Each sample was partitioned into three clusters. To facilitate comparison, each cluster was annotated with a region label (PC, PP, or Mid), determined by integrating information from cluster-specific marker genes and H&E staining images. Furthermore, to compare gene expression profiles across clusters, we identified differentially expressed genes using fold change analysis and the Wilcoxon rank sum test. This approach allowed us to highlight genes showing significant expression differences among all or selected clusters, providing insights into biological variations across spatial regions in the tissue samples.

For differential gene analysis across different samples, we initially integrated all samples using the combat function to mitigate batch effects.(Stuart et al., 2019) Subsequently, differential expression analysis of variable genes was

The Single-cell regulatory network inference and clustering (SCENIC) workflow comprises three main steps: coexpression analysis, target gene motif enrichment analysis, and regulon activity assessment. The analysis employed pySCENIC (version 0.12.1)(van de Sande et al., 2020) with default parameters, using the raw count matrix from all samples as input. Co-expression modules were initially identified, and the interaction strength between transcription factors (TFs) and their target genes was evaluated using GRNBoost.(Aibar et al., 2017) Each coexpressed module underwent motif enrichment analysis using RcisTarget with a rank threshold of 3000, to identify modules where the TF motif was significantly enriched among targets.(Aibar et al., 2017) Only modules meeting these criteria were retained, establishing TFs along with their potential direct targets as regulons. The activity of each regulon within each cell was assessed using AUCell. For visualization, average regulon activity scores (AUC) were calculated for each cluster.(Aibar et al., 2017) A rank plot of regulons was generated using ggplot2. Additionally, the gene expression network related to the target genes of the TFs of interest was visualized using the nx.draw function.

To annotate cell clusters in the single-nucleus RNA-seq (snRNA-seq) data, we utilized a curated list of known marker genes corresponding to liver cell lineages, as reported in Multimodal Decoding of Human Liver Regeneration. Using the AddModuleScore function in Seurat, we calculated signature scores to assign cell lineage identities. Clusters identified as primarily composed of cycling cells were further reclustered to segregate them into their constituent lineages. This process was repeated iteratively for each identified lineage. A cleansing step was included to remove clusters enriched for nuclei identified as doublets or overexpressing marker genes from multiple lineages. Hepatocytes were subsequently extracted for further reclustering, using resolution (res) = 0.2 and 80 principal components (npc). To annotate the hepatocyte clusters, we applied a curated list of known zonation-specific marker genes for hepatocytes.

Cut&Run was conducted on mouse liver tissue samples from mice injected with APAP for 6 hours using the Cut&Run assay kit (CST, 86652) following the manufacturer’s protocol. Initially, 1 mg of fresh tissue was weighed for each antibody/MNase reaction, with an additional 1 mg for the input sample. Reactions included positive and negative controls. Tissues were fixed with 1 ml of 0.1% formaldehyde for 10 minutes and quenched with glycine for 5 minutes. After washing with PBS, tissues were resuspended in 1 ml of 1X Wash Buffer (+ spermidine + protease inhibitor cocktail) and transferred to a Dounce homogenizer. Tissue was homogenized into a single-cell suspension with 10-15 strokes until no tissue chunks remained.

For the Cut&Run assay to identify Chop binding sites, samples were bound to concanavalin A beads and incubated overnight at 4°C with the following primary antibodies: 0.5 μg CHOP (CST, 2895S, 1:100), 2 μl positive control Tri-Methyl-Histone H3 (Lys4) (CST, 9751), and 5 μl negative control Rabbit (DA1E) mAb IgG XP® Isotype Control (CST, 66362). The pAG MNase enzyme was activated by adding cold calcium chloride and incubating at 4°C for 30 minutes. The reaction was terminated with 1X stop buffer at 37°C for 10 minutes without shaking to release DNA fragments into the solution.

To reverse crosslinks in fixed tissue samples, samples were brought to room temperature and treated with 10% SDS Solution (BBI, B548118-0100) to a final concentration of 0.1%, along with proteinase K (20 mg/ml). DNA Extraction Buffer (+ Proteinase K + RNAse A) was added to the input sample. Cells were lysed and chromatin fragmented by sonication using a Covaris ME220 contact ultrasound apparatus on ice, with optimal conditions generating chromatin fragments ranging from 100 to 600 bp. DNA was purified using phenol/chloroform extraction followed by ethanol precipitation, and DNA quantification was performed by qPCR. For Cut&Run, purified DNA was used to prepare sequencing libraries with the NEB Next Ultra II DNA Library Prep Kit for Illumina (7645S and 7335L) and sequenced on an Illumina Nova-Seq 6000 sequencer to obtain 150 bp paired-end reads.

For visualization purposes, BW files were generated using Integrative Genomics Viewer (IGV) software (Robinson et al., 2011), and bigwig files were created using Deeptools (version 3.5.1) with the ‘bamCoverage’ module (bamCoverage --binSize 100) (Ramírez et al., 2016). For each biological replicate and its corresponding IgG control, peaks were called using macs2 (version 2.2.7) with the command macs2 callpeak -f BAMPE --qvalue 0.1 --keep-dup (Zhang et al., 2008). To annotate the identified peaks, ChIPseeker (version 1.26.2) in R was utilized (Yu et al., 2015).

The zonation of protein (such as Ki67, Chop, Atf4) -positive nuclei was quantified using the following method: the position index (P.I.) was calculated based on distances to the nearest CV (x), portal vein (PV) (y), and the distance between CV and PV (z), utilizing the law of cosines. The formula employed was P.I. = (x^2 + z^2 - y^2) / (2z^2). This approach aligns with the methodology described by Lin et al (Lin et al., 2018).

All experimental data are presented as the mean ± SD. Statistical analyses were performed using GraphPad Prism (v8.0.2), with appropriate tests selected based on experimental design: unpaired two-tailed Student’s t-tests for comparisons between two groups, and one-way ANOVA for comparisons involving three or more groups. The number of animals (“n”) used in each experiment is indicated in the Figures and corresponding legends. For quantification of liver sections, three to five random pericentral and periportal fields of each liver sample, unless specified otherwise, were imaged and quantified using Image J.

Cut&run data are accessible (GSE272565). ST data are accessible (GSE272564). This paper does not report original code.

We thank Bin Qi (Yunnan University) for suggestions and discussion. We thank Dr. Yin Hao (Wuhan University) for providing us the pHelper and pAAV2/8 plasmids. We thank Hui Yang (Institute of Neuroscience, SBS, CAS) for providing us the plasmids related to CasRx system. We thank Yonglong Wei (Yunnan University) for helping in quantification of spatial distribution.

This work was supported by National Natural Science Foundation of China (82570734 to Z.S.), Yunnan Provincial Science and Technology Department (C619300A086 to Z.S.), National Natural Science Foundation of China (32170662 to C.P.), and Yunnan Fundamental Research Project (202401AS070131 to C.P.).

Y.Y.Z. performed the experiments, analyzed the data, and wrote the methods and figure legends. C.X.D. initially analyzed the sequencing data, while B.C. continued the sequencing data analysis, performed the experiments, wrote the methods and figure legends. J.H. prepared the ST samples. Y.N.L performed the experiments. S.L. constructed the HPx mouse model. W.J.L. analyzed the sequencing data during revision. C.P. supervised the ST data analysis by C.X.D., participated in the initial study design, and revised the manuscript. Z.S. initiated, organized, and designed the study, and wrote the manuscript.

Zhao Shan https://orcid.org/0000-0001-5064-1023

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All data generated or analysed during this study are included in the manuscript and supporting files; source data files have been provided.