kotahi

eLife Assessment

This study addresses an important question in liver biology: how zonal hepatocytes balance survival and proliferation following injury? The authors propose that a mid-zone Atf4-Chop axis to Btg2 program temporarily suppresses proliferation to promote survival after a variety of chemical and surgical liver injury models. The authors provide evidence that some zones mount tailored stress responses, which ultimately promote regeneration and liver healing; however, the "mid-zone" changes with different injury models, making it difficult to conclude that the ATF4-CHOP response is specific to this zone in all injury contexts. In addition, it is possible that Atf4 and Btg2 overexpression could lead to Cyp2e1 suppression, which could reduce the extent of injury after CCl4 or APAP. To some extent, these points make the strength of the evidence incomplete, but do not entirely detract from the significance of the study, which is underscored by the helpful observation that there are zone-specific stress responses that mediate liver regeneration and survival.

Reviewer #1 (Public review):

Summary:

The authors present evidence that during acetaminophen (APAP)-induced liver injury, mid-zone hepatocytes activate an integrated stress response (ISR) program via Atf4 and Chop, leading to induction of Btg2. This program suppresses proliferation in the early phase of injury, prioritizing hepatocyte survival before regeneration begins. The study uses spatial transcriptomics, immunohistochemistry, CUT&RUN, and AAV overexpression to support this model.

Strengths:

(1) Innovative use of spatial transcriptomics to capture zonal differences in hepatocyte stress responses.

(2) Identification of a mid-zone specific ISR signature and candidate downstream regulator Btg2

(3) Functional experiments with Atf4-Chop-Btg2 modulation provide causal evidence linking ISR activation to proliferation inhibition.

(4) Conceptually significant model that hepatocytes actively balance survival and regeneration dynamically in a zone-specific manner.

(5) Multiple models validation of the finding

(6) The functional link of such zone2 phenotype is added.

Reviewer #2 (Public review):

The manuscript reports protection of midlobular hepatocytes from APAP toxicity by activation of Atf4-CHOP (Ddit3)-mediated cell cycle arrest and stress response. The authors acknowledge that their finding is unexpected because CHOP typically induces cell death. Therefore, they functionally validate several aspects of the proposed Atf4-CHOP mechanism. Along these lines, the mitigation of APAP toxicity by AAV expression of Atf4 or Btg2, the latter identified as CHOP effector, is impressive. Whether Atf4 indeed acts through CHOP and whether midlobular hepatocytes are protected because of cell cycle arrest is less clear. These and other criticisms are described in the following.

Major points:

(1) Starting with the basics, one wonders why midlobular hepatocytes manage to mount a defensive response to APAP, but PC hepatocytes don't. Is this because midlobular hepatocytes express the relevant Cyps (2e1 but also 1a2 and 3a11) at lower levels, which mitigates toxicity and buys them time? This would be supported by F2A but not by F3B, at least not for the most important Cyp2e1. A moderate difference is shown for Cyp1a2 expression in F3D but is that enough to explain the different fates? Or are additional post-transcriptional effects on these Cyps at work? The difference in baseline Cyp2e1 expression between F2A and F3B remains unexplained after revision.

(2) The evidence presented in support of cell cycle arrest of midlobular hepatocytes is not fully convincing: there is no overt difference in S and G2/M gene scores in F2F; the marker genes used for S phase and G1 to S progression in F2G are unusual. Along these lines, one wonders if spatial transcriptomics confirmed the Ki67 immunostaining results in F1 also for specific zones, not only overall as shown in F2E? In contrast to the revised discussion, the abstract does not reflect that limited evidence for a cell cycle arrest in pericentral hepatocytes was found.

(3) The authors conclude in line 364 that halting of proliferation by Btg2 favors survival, which raises the question of whether Btg2 knockout causes death in midlobular hepatocytes in F6K. Data addressing this question, that is, localization and extent of tissue necrosis and ALT levels after APAP, are missing. The efficiency of knockout of Btg2 is also not given. Additional Btg2 knockout data support its proposed role in the revised manuscript.

(4) Related to the previous question, the BTG2 immunostaining in F6F is not convincing when compared to F6D. One also wonders if it is necessary to apply APAP to find induction of BTG2 by AAV-Ddit3? The BTG2 immunostaining remains weak, not only in in F6F but now also in F6D of the revised manuscript, which together with lack of high-resolution immunostaining of AAV-Ddit3-induced BTG2 in the absence of APAP results in limited support for the conclusion that APAP promotes nuclear localization of BTG2.

(5) Related to the previous question, the proposed Atf4-Ddit3 axis is challenged by the lack of midlobular induction of Atf4 in the APAP scRNA-seq data published by another group presented in S4F and G. Further analysis of AAV-Atf4 samples generated for F5 could address if it is really Atf4 that acts on Ddit3 in APAP toxicity. The extended list of transcription factors (from 30 to 50) includes Atf4 but direct evidence for an interaction with Ddit3 is missing from the revised manuscript.

(6) Related to the previous question, the ATF4 immunostaining in F5A doesn't look convincing, with many brown pigments appearing to be outside of the nucleus. The ATF4 immunostaining after APAP challenge remains weak.

(7) It is not ruled out that AAV expression of Atf4 or Btg2 reduces hepatocyte sensitivity to APAP by affecting expression of the Cyps needed for activation. In other words, does AAV-Atf4 or AAV-Btg2 change the expression of any of the Cyps relevant to APAP in the 3 weeks before APAP application (F5B)? S5A of the revised manuscript rules out loss of Cyp2e1 expression as a confounding factor.

(8) It is laudable that the authors tried to extend their findings to human by using snRNA-seq data from a published study (line 391) but it is unclear why they didn't analyze all 10 patients in that study but instead focused on 2 and stated that this small sample number prevented drawing definitive conclusions and could therefore only be mentioned in the discussion. The revised manuscript continues to focus on rare spatial transcriptomics analyses of patients with APAP toxicity although more snRNA-seq analyses of such patients are available which should also allow for analysis of hepatocyte zonation.

Comments on revised version.

After revision, the proposed role of Btg2 is substantiated but it remains unclear why midlobular hepatocytes don't proliferate after APAP challenge and whether the observed protective effects are indeed mediated by Atf4 acting directly through CHOP.

Author response:

The following is the authors’ response to the original reviews.

Public Reviews:

Reviewer #1 (Public review):

(1) Zonation definition under injury has been shown to be sustained broadly, but is not sufficiently validated and quantified, especially considering the resolution of the 10x Visium system and the potential variation of outcomes based on how to define zones.

We thank the reviewer for these insightful suggestions. In this study, 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., Gs, Cyp2e1, Oat, Cyp1a2, Apoe). The Mid zone comprised regions with intermediate expression of PC and PP markers and elevated levels of Igfbp2 and Hamp (Revised Figure 2A and S1C). Following APAP-induced injury (3 h, 6 h), 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) (Revised Figure 2A and S1C). To validate and quantify our zonation approach, we compared it with classical nine even layers from central vein (CV) to portal vein (PV). Immunostaining and quantification for Cyp2f2 (a PP marker), p62 (the protein product of Sqstm1, a Mid marker during early liver injury), Glutamine Synthetase (GS, the protein product of Glul, a PC marker) further corroborated zone definitions at each time point, showing correspondence of our PC (layers 1–2), Mid (layers 3–6), and PP (layers 7–9) (Revised Figure 2 B-D) (Revised manuscript, page 5, lines 119–131, page 6-7, line 174-182).

(2) The model is built entirely in APAP injury, which specifically targets pericentral hepatocytes. It remains unclear whether the proposed mechanism applies to other liver injuries (e.g., partial hepatectomy, CCl4).

We thank the reviewer for this insightful comment. 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. 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 (Revised Figure S6A–G). In PHx model (examined 24 h after surgery), ISR activation was similarly observed around ischemic injury sites, with increased p‑eIF2α, Atf4, Chop, and Btg2 expression and undetectable Ki67 expression (Revised Figure S7A–G). Together, these additional models suggest that the proposed mechanism may be applicable to other types of liver injury (Revised manuscript, page 15, Line 410-426).

(3) Baseline proliferation appears higher than expected in homeostasis (Figure 1B), and fold change analysis (not absolute counts) may be needed to assess zonal proliferation suppression (Figure 1D).

We thank the reviewer for this insightful comment. The baseline proliferation rates observed in our study are consistent with previously reported zonal distributions (PMID: 33632817; PMID: 33632818), with approximately 70% of proliferating hepatocytes located in zone 2, 20% in zone 3, and 10% in zone 1 under homeostatic conditions. To further address the reviewer’s concern, we performed a fold-change analysis of Ki-67+hepatocytes across different zones. This analysis revealed that only the mid (zone 2) and pericentral regions exhibited significant changes, whereas no statistically significant differences were observed in the other zones (as shown in Author response image 1). Importantly, when considered together with the absolute cell counts, these results indicate that the apparent suppression of proliferation is most pronounced in the mid zone, likely due to its relatively higher baseline proliferation under homeostatic conditions. In contrast, this effect is less evident in the fold-change analysis, as zones with low baseline proliferation show limited dynamic range for detecting relative changes.

Author response image 1.

Fold changes of Ki67-positive cells across liver zones (PC, Mid, PP) at 0, 3, 6, 12 and 24 h post-APAP. Fold change was the number of Ki67-positive cells in the three regions at each time point after APAP treatment divided by the number of positive cells in each region at 0 hour post-APAP. (a) denotes significance between PC and Mid regions, (b) denotes significance between PC and PP regions, and (c) denotes significance between Mid and PP regions.

(4) AAV-based overexpression raises potential confounds (altered CYP activity before injury) and shows incomplete penetrance that is not quantified (Figure 5 - Figure 6).

We thank the reviewer for raising these important points. We measured basal Cyp2e1 protein levels by western blot in AAV‑EGFP, AAV‑Atf4, and AAV‑Btg2 mice without APAP treatment. Compared to AAV‑EGFP controls, Cyp2e1 expression was modestly reduced in the Atf4 and Btg2 groups, respectively (Revised Figure S5A). Although we assessed protein abundance rather than enzymatic activity directly, Cyp2e1 protein levels under basal conditions generally correlate well with activity. Published studies demonstrate that robust protection against APAP hepatotoxicity typically requires >50% suppression of CYP2E1 activity (PMID: 35145060; PMID: 30151903). The minor reductions we observed are therefore far below the threshold needed to explain the 70–90% decreases in serum ALT conferred by Atf4 or Btg2 overexpression (Revised Figures 5D and 6I). Accordingly, altered CYP2E1 activity is unlikely to represent a significant confound in our model.

We quantified transduction efficiency by immunohistochemical detection of the respective transgene proteins and determined the percentage of positive hepatocytes. At a dose of 1.2 × 1011 viral genomes per animal, average transduction rates were 32% (EGFP), 18% (Atf4), and 23% (Btg2) (Revised 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; Revised Figure S5C), demonstrating that greater transgene expression associates with stronger protection. Although these average transduction rates appear modest relative to the 70–90% reduction in ALT, this apparent disproportion is consistent with the known tendency of AAV‑TBG vectors to transduce hepatocytes preferentially in the pericentral region—the same zone where APAP‑induced necrosis initiates. Pericentral enrichment of transgene expression could thus provide disproportionate protection by targeting the most vulnerable cells. These data are now included in Revised Figure S5A–C and detailed in the Results (page 14, lines 383–399).

(5) The functional link between proliferation suppression and improved survival is inferred, but direct survival /injury readouts are limited.

We thank the reviewer for this insightful comment. To more directly evaluate the functional link between proliferation control and liver injury, we manipulated Btg2, a downstream effector of the Atf4–Chop axis and a known inhibitor of cell proliferation. Knockdown of Btg2 using AAV8–CasRx achieved a moderate (~30%) reduction in Btg2 expression (Revised Figure S5D). Despite this partial knockdown efficiency, we observed a clear exacerbation of liver injury, as evidenced by an approximately 2-fold increase in serum ALT levels and a ~1.5-fold expansion of necrotic areas. In parallel, hepatocyte proliferation was significantly increased (~1.8-fold increase in Ki67⁺ hepatocytes) compared to control mice (Revised Figure 6K–N). Conversely, Btg2 overexpression produced the opposite phenotype, markedly attenuating liver injury while suppressing hepatocyte proliferation (Revised Figure 6G–J). Together, these gain- and loss-of-function data provide direct evidence linking proliferation control to injury severity, thereby supporting a causal relationship between suppressed proliferation and improved liver outcomes (Revised manuscript, page 14, lines 399–406).

Reviewer #2 (Public Review):

(1) Starting with the basics, one wonders why midlobular hepatocytes manage to mount a defensive response to APAP but pericentral hepatocytes don't. Is this because midlobular hepatocytes express the relevant Cyps (2e1, but also 1a2 and 3a11) at lower levels, which mitigates toxicity and buys them time? This would be supported by F2A but not by F3B, at least not for the most important Cyp2e1. A moderate difference is shown for Cyp1a2 expression in F3D, but is that enough to explain the different fates? Or are additional post-transcriptional effects on these Cyps at work?

We thank the reviewer for this important question. We fully agree that the differential susceptibility between mid‑zone and pericentral (PC) hepatocytes is likely rooted in the zonal gradient of cytochrome P450 expression. Our spatial transcriptomics data (Revised Figure 2A) show that mid‑zone hepatocytes express Cyp2e1, Cyp1a2, and Cyp3a11 at levels intermediate between PC and periportal (PP) zones. This intermediate expression may generate sufficient NAPQI to activate stress signaling but not so much as to cause immediate mitochondrial collapse, thus “buying time” for adaptive responses. We also appreciate the reviewer’s observation that Cyp2e1 mRNA levels remain highest in the PC zone even after APAP (Revised Figure 3B). However, mRNA abundance does not necessarily reflect functional protein level. In the PC zone, massive necrosis rapidly compromises cellular integrity; as shown in Revised Figure 3D, Cyp1a2 protein declines sharply around the central vein, and we observed similar degradation for Cyp2e1 (data not shown). Consequently, despite sustained Cyp2e1 transcripts, PC hepatocytes are unable to mount an effective stress response because they are already undergoing cell death. By contrast, mid‑zone hepatocytes retain sufficient metabolic capacity to activate the Atf4‑Chop axis while preserving cellular function.

(2) The evidence presented in support of cell cycle arrest of midlobular hepatocytes is not fully convincing: there is no overt difference in S and G2/M gene scores in F2F; the marker genes used for S phase and G1 to S progression in F2G are unusual. Along these lines, one wonders if spatial transcriptomics confirmed the Ki67 immunostaining results in F1 also for specific zones, not only overall, as shown in F2E?

We thank the reviewer for these important observations. We agree that the current spatial transcriptomics (ST) data alone do not provide sufficiently strong support for this conclusion. The limited sensitivity of ST for detecting rare proliferative events further constrains its utility in this context. At baseline, only ~1% of ST spots are Ki67-positive (Revised Figure S1I), and this fraction becomes even lower during the early phase following APAP injury. As a result, there are insufficient Ki67+ spots to robustly assess zonal distribution using ST, which precludes a reliable spatial validation of proliferation patterns at this resolution. For this reason, our primary evidence for zonal proliferation dynamics relies on Ki67 immunohistochemistry (Revised Figure 1), which provides single-cell resolution and higher sensitivity. These data show a marked reduction in Ki67+ hepatocytes specifically in the midlobular zone at 3-6 hours post-APAP, supporting a transient suppression of proliferation in this region. In addition, we agree that the transcriptional evidence for cell cycle arrest was not strong the S and G2/M scores showed no overt difference, and the gene sets used were suboptimal. We have therefore moved these analyses to the supplement and toned down the claims. We have also clarified this limitation in the manuscript (Revised manuscript, page 18, line 518-524)

(3) The authors conclude in line 364 that halting of proliferation by Btg2 favors survival, which raises the question of whether Btg2 knockout causes death in midlobular hepatocytes in F6K. Data addressing this question, that is, the localization and extent of tissue necrosis and ALT levels after APAP, are missing. The efficiency of the knockout of Btg2 is also not given.

We thank the reviewer for this insightful comment. We have included the missing data. Knockdown of Btg2 using AAV8‑CasRx achieved a moderate (~30%) reduction in Btg2 expression (Revised Figure S5D). Despite this partial efficiency, we observed a significant increase in serum ALT levels (~2‑fold), expansion of necrotic areas (~1.5‑fold), and a marked increase in Ki67+hepatocytes (~1.8‑fold) compared to control mice (Revised Figure 6K–N, Revised manuscript, page 14, line 399-406).

(4) Related to the previous question, the BTG2 immunostaining in F6F is not convincing when compared to F6D. One also wonders if it is necessary to apply APAP to find induction of BTG2 by AAV-Ddit3?

We thank the reviewer for this insightful comment. We have included an inset of the original image to better show BTG2 staining in revised Figure 6F. During our study, we tested BTG2 expression in mice transduced with AAV‑TBG‑EGFP or AAV‑TBG‑BTG2 for three weeks without APAP challenge. We observed that BTG2 in these non‑injured livers was predominantly cytoplasmic (Author response image 2), contrasting with the nuclear localization seen after APAP treatment (Figure 6F). Regarding whether it is necessary to apply APAP to find induction of BTG2 by AAV-Ddit3, we think Ddit3 promotes BTG2 expression (as shown in revised Figure F6F), but APAP is necessary for its nuclear translocation.

Author response image 2.

Immunohistochemical detection of Btg2 in liver tissue from mice transduced with AAV-TBG-EGFP or AAV-TBG-Btg2 for 3 weeks without APAP treatment.

(5) Related to the previous question, the proposed Atf4-Ddit3 axis is challenged by the lack of midlobular induction of Atf4 in the APAP scRNA-seq data published by another group, presented in S4F and G. Further analysis of AAV-Atf4 samples generated for F5 could address whether it is really Atf4 that acts on Ddit3 in APAP toxicity.

We thank the reviewer for this insightful comment. We agree that Atf4 was not among the top 30 active transcription factors in our initial analysis; however, when we extended the list to the top 50, Atf4 was included. We have therefore updated Revised Figures S4F and G to show the top 50 transcription factors. We also appreciate the reviewer’s suggestion to further investigate whether Atf4 directly acts on Ddit3 in the context of APAP toxicity. While this still shows a less pronounced midlobular enrichment for Atf4 compared with Ddit3, we sought additional evidence for a functional Atf4-Ddit3 link. In primary hepatocytes treated with APAP, we observed nuclear co‑localization of Atf4 and Ddit3 (Author response image 3A) and increased nuclear protein levels of both factors (Author response image 3B), supporting their potential cooperative role. We agree that direct analysis of AAV‑Atf4 samples generated for Figure 5 would provide more definitive evidence; unfortunately, co‑staining for Atf4 and Ddit3 on those tissue sections didn’t work well.

Author response image 3.

Subcellular localization of Atf4 and Chop in primary hepatocytes following APAP treatment. (A) Immunofluorescence staining of Atf4 and Chop in primary hepatocytes treated with 10 mM APAP for 6 hours or left untreated (UT). Nuclei were counterstained with DAPI. Scale bar as indicated. (B) Primary hepatocytes were treated with 0, 5, or 10 mM APAP for 6 hours. Cytoplasmic and nuclear fractions were isolated and analyzed by western blot. Lamin B1 and α-Tubulin were used as markers for the nucleus and cytoplasm, respectively

(6) Related to the previous question, the ATF4 immunostaining in F5A doesn't look convincing, with many brown pigments appearing to be outside of the nucleus.

We thank the reviewer for this helpful comment. To better demonstrate ATF4 nuclear localization, we have added enlarged insets of the original representative images in revised Figure 5A. These magnified views more clearly show nuclear ATF4 staining after APAP treatment, addressing the concern about extranuclear signal.

(7) It is not ruled out that AAV expression of Atf4 or Btg2 reduces hepatocyte sensitivity to APAP by affecting the expression of the Cyps needed for activation. In other words, does AAV-Atf4 or AAV-Btg2 change the expression of any of the Cyps relevant to APAP in the 3 weeks before APAP application (F5B)?

We thank the reviewer for raising these important points. We measured basal Cyp2e1 protein levels by western blot in AAV‑EGFP, AAV‑Atf4, and AAV‑Btg2 mice without APAP treatment. Compared to AAV‑EGFP controls, Cyp2e1 expression was modestly reduced in the Atf4 and Btg2 groups, respectively (Revised Figure S5A). Although we assessed protein abundance rather than enzymatic activity directly, Cyp2e1 protein levels under basal conditions generally correlate well with activity. Published studies demonstrate that robust protection against APAP hepatotoxicity typically requires >50% suppression of CYP2E1 activity (PMID: 35145060; PMID: 30151903). The minor reductions we observed are therefore far below the threshold needed to explain the 70–90% decreases in serum ALT conferred by Atf4 or Btg2 overexpression (Revised Figures 5D and 6I). Accordingly, altered CYP2E1 activity is unlikely to represent a significant confound in our model.

(8) It is laudable that the authors tried to extend their findings to humans by using snRNA-seq data from a published study (line 391), but it is unclear why they didn't analyze all 10 patients in that study but instead focused on 2 and stated that this small sample number prevented drawing definitive conclusions and could therefore only be mentioned in the discussion.

We thank the reviewer for this clarification. The analysis mentioned in line 391 originally referred to spatial transcriptomics (ST) data from two ALF patients, not snRNA-seq. For the snRNA-seq dataset, we analyzed all 10 patients, but snRNA-seq lacks spatial resolution and cannot reliably assign zonal identity. We stipulate that snRNA-seq requires viable cells and thus likely excludes necrotic/peri-necrotic areas. Therefore, direct zonal comparison with our ST data was not possible. We have now clarified this in the revised manuscript (Revised manuscript, page 18, line 510-519).

Reviewer #3 (Public Review):

The main concern is that the overexpression of ATF4 and DDIT3 is causing reduced cell death and damage by APAP. This makes it harder to understand if these genes are truly increasing survival or if they are just reducing the injury caused by APAP. It may be better to perform overexpression immediately after, or at the same time as APAP delivery. Alternatively, loss-of-function experiments using AAV-shRNAs against these targets could be useful.

We thank the reviewer for raising this important point. We agree that overexpression prior to APAP administration leaves open the question of whether the observed protection reflects true cytoprotection or simply reduced initiation of injury. To address this, we pursued loss‑of‑function approaches. Due to their very low basal expression, AAV‑shRNA‑mediated knockdown of endogenous Atf4 and Ddit3 proved inefficient. We therefore targeted Btg2, a downstream mediator of Ddit3 that inhibits proliferation. Knockdown of Btg2 resulted in a significant increase in APAP‑induced liver injury, as evidenced by elevated ALT levels and expanded necrotic areas (Revised Figure 6K-N). These results indicate that the ATF4‑DDIT3‑BTG2 axis limits hepatocellular damage, consistent with a protective role. We have clarified this point in the revised manuscript (page 15, line 407-414)

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

(1) Clarify how zones were defined when necrosis disrupted pericentral areas. Provide marker validation across time and whether necrotic spots are excluded or not from zonal analysis.

We thank the reviewer for these insightful suggestions. In this study, 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., Gs, Cyp2e1, Oat, Cyp1a2, Apoe). The Mid zone comprised regions with intermediate expression of PC and PP markers and elevated levels of Igfbp2 and Hamp (Revised Figure 2A and S1C). Following APAP-induced injury (3 h, 6 h), 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) (Revised Figure 2A and S1C). To validate and quantify our zonation approach, we compared it with classical nine even layers from central vein (CV) to portal vein (PV). Immunostaining and quantification for Cyp2f2 (a PP marker), p62 (the protein product of Sqstm1, a Mid marker during early liver injury), Glutamine Synthetase (GS, the protein product of Glul, a PC marker) further corroborated zone definitions at each time point, showing correspondence of our PC (layers 1–2), Mid (layers 3–6), and PP (layers 7–9) (Revised Figure 2 B-D) (Revised manuscript, page 5, lines 119–131, page 6-7, line 174-182).

(2) Test whether the ISR-Btg2 program applies in other models; even targeted validation via qPCR and IF would be valuable.

We thank the reviewer for this insightful comment. 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. 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 (Revised Figure S6A–G). In PHx model (examined 24 h after surgery), ISR activation was similarly observed around ischemic injury sites, with increased p‑eIF2α, Atf4, Chop, and Btg2 expression and undetectable Ki67 expression (Revised Figure S7A–G). Together, these additional models suggest that the proposed mechanism may be applicable to other types of liver injury (Revised manuscript, page 15, Line 410-426).

(3) Proliferation quantification in liver sections in Figure 1: how to define the zones and why, at the basal level, there is a high proliferation rate in the mid zone? From Figure 1B-C, all three zones showed decreased hepatocyte proliferation, although the mid zone had a higher baseline. Will the mid-zone stand out by converting to the fold change of Ki-67+ hepatocytes decrease?

We thank the reviewer for these insightful comments. To define the pericentral (PC), mid, and periportal (PP) zones, we adopted the classical nine‑layer model of the hepatic lobule described by Lin et al. (PMID: 29618815). Layers 1–2 were designated as the PC zone, layers 3–6 as the mid zone, and layers 7–9 as the PP zone. For quantitative zonal distribution of protein‑positive nuclei (e.g., Ki67, CHOP, ATF4), we calculated a position index (P.I.) based on distances to the nearest central vein (CV) and portal vein (PV), using the law of cosines: P.I. = (x2 + z2 – y2) / (2z2), where x = distance to CV, y = distance to PV, and z = distance between CV and PV. This quantification method has now been included in the Methods section (Revised manuscript, page 33, line 880-885). Consistent with previous reports (PMID: 33632817; PMID: 33632818), we observed a higher baseline proliferation rate in the mid zone, where approximately 70% of proliferating hepatocytes reside under basal conditions, compared to 10% in zone 1 and 20% in zone 3. However, when analyzing the fold change in Ki-67+ hepatocytes, only Mid and PC region showed significant difference in Ki-67+ hepatocytes, other zones showed no significant differences (as shown in the fold-change results in Author response image 1), indicating that the mid zone does not stand out in the fold change analysis. See Author response image 1.

(4) The authors need to strengthen the causal chain with rescue experiments, e.g., Atf4/Chop overexpression and Btg2 knockdown. Link proliferation suppression to survival/ALT directly.

We thank the reviewer for these constructive comments. Besides existing data from Figure 5 (Atf4 overexpression), we included Btg2 knockdown data in the revised Figure. Knockdown of Btg2 using AAV8‑CasRx achieved a moderate (~30%) reduction in Btg2 expression (Revised Figure S5D). Despite this partial efficiency, we observed a significant increase in serum ALT levels (~2‑fold), expansion of necrotic areas (~1.5‑fold), and a marked increase in Ki67+ hepatocytes (~1.8‑fold) compared to control mice (Revised Figure 6K–N) (Revised manuscript, page 14, lines 399–406).

(5) Transduction efficiency, distribution, and expression levels via the AAV overexpression need to be quantified. Key CYP genes in the APAP metabolic pathway need to be assessed to exclude confounds.

We thank the reviewer for raising these important points. We measured basal Cyp2e1 protein levels by western blot in AAV‑EGFP, AAV‑Atf4, and AAV‑Btg2 mice without APAP treatment. Compared to AAV‑EGFP controls, Cyp2e1 expression was modestly reduced in the Atf4 and Btg2 groups, respectively (Revised Figure S5A). Although we assessed protein abundance rather than enzymatic activity directly, Cyp2e1 protein levels under basal conditions generally correlate well with activity. Published studies demonstrate that robust protection against APAP hepatotoxicity typically requires >50% suppression of CYP2E1 activity (PMID: 35145060; PMID: 30151903). The minor reductions we observed are therefore far below the threshold needed to explain the 70–90% decreases in serum ALT conferred by Atf4 or Btg2 overexpression (Revised Figures 5D and 6I). Accordingly, altered CYP2E1 activity is unlikely to represent a significant confound in our model.

We quantified transduction efficiency by immunohistochemical detection of the respective transgene proteins and determined the percentage of positive hepatocytes. At a dose of 1.2 × 1011 viral genomes per animal, average transduction rates were 32% (EGFP), 18% (Atf4), and 23% (Btg2) (Revised 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; Revised Figure S5C), demonstrating that greater transgene expression associates with stronger protection. Although these average transduction rates appear modest relative to the 70–90% reduction in ALT, this apparent disproportion is consistent with the known tendency of AAV‑TBG vectors to transduce hepatocytes preferentially in the pericentral region—the same zone where APAP‑induced necrosis initiates. Pericentral enrichment of transgene expression could thus provide disproportionate protection by targeting the most vulnerable cells. These data are now included in Revised Figure S5A–C and detailed in the Results (page 14, lines 383–399).

(6) The authors claim that the requirement of the Atf4/Chop at the early stage of APAP injury protects hepatocytes from proliferation for survival. What is the consequence if we remove the protective mechanism?

We thank the reviewer for this insightful question. In our model, early induction of Atf4 and Chop functions as a cell survival checkpoint. Removal of this protective mechanism is predicted to result in two deleterious outcomes: (1) Acute exacerbation of necrosis due to the inability of hepatocytes to manage stress-induced bioenergetic demands, and (2) Impaired long-term regeneration due to depletion of the surviving cell pool. We directly tested the acute prediction (< 24 h) in Author response image 4. We deleted Ddit3 specifically in hepatocytes. Initial attempts using AAV-CasRx failed due to negligible baseline Atf4/Chop expression in healthy liver, preventing effective knockdown. We therefore generated hepatocyte-specific Ddit3 knockout mice (Alb∆Ddit3; Author response image 4B). Immunohistochemistry confirmed APAP-induced Chop induction occurs primarily in the centrilobular zone by 6 h (Author response image 4A). Following a two-dose APAP regimen (Author response image 4C), Alb∆Ddit3 mice displayed significantly larger areas of centrilobular necrosis compared to Ddit3fl/fl controls (Author response image 4D; **p < 0.01). Thus, hepatocyte-intrinsic Chop limits acute APAP injury, consistent with its proposed early protective role.

Author response image 4.

Hepatocyte-specific deletion of Ddit3 exacerbates APAP-induced liver injury. (A) Immunohistochemical staining of Chop in liver sections at 0,3 and 6 h post-APAP. Red arrows indicate Chop-positive hepatocytes. Scale bar = 50μm. Quantification of zonal distribution of Chop-positive cells in liver sections at 6 h post-APAP is conducted . The statistic is the percentage of Chop-positive hepatocytes in each layer over the total number of Chop-positive hepatocytes. n=3 mice. (B)The construction, genotyping strategy and genotyping results of Alb∆Ddit3 mice. P: positive control; WT: Wild-type; Neg: Blank control(ddH2O). (C) Schematic figure illustrating the experimental strategy for the administration of two doses of APAP to Ddit3fl/fl and Alb∆Ddit3 mice. (D) H&E staining showing liver morphology from Ddit3fl/fl and Alb∆Ddit3 mice at 6 h post-second dose of APAP. Injured area is outlined by black dashed lines. Scale bars = 200 μm. The percentage of injury area is quantified. n = 3- 4 mice/group. Data are represented as means ± SD; *p < 0.05; **p < 0.01; ***p < 0.001; ****p < 0.0001; ns, not significant.

(7) Is there any human relevance to the sensitivity of APAP injury regarding the Atf4/Chop axis?

We thank the reviewer for this insightful comment. During our study, we analyzed a spatial transcriptomics dataset from APAP patients. 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 (Author response image 5). This suggests that this axis may also be involved in the response to APAP injury in humans. However, given the limited sample size, definitive conclusions cannot be drawn at this stage. We have now included this point in the Discussion section (Revised manuscript, page 18, line 510-519).

Author response image 5.

Spatial transcriptomics (GSE223561) reveals zonal gene expression changes in APAP patients. Heatmap of ISR, cell death, and cell cycle gene expression across zonal regions in healthy versus APAP‑treated human livers.

(8) Several IHC stainings have a weak signal and need inserts to zoom in for a clear view of the positive signals. Figure 5A, E, G, and Figure 6D, F.

We thank the reviewer for this observation. We agree that the immunostaining signals for several target genes are relatively weak, which reflects their low endogenous expression levels. To address this, we have included higher-magnification insets in the indicated panels (Revised Figure 5A, E, G and Figure 6D, F) to show the positive signals.

Reviewer #2 (Recommendations for the authors):

(1) What is the functional classification of DEG in F2A based on? GO terms?

We thank the reviewer for this constructive question. The functional classification of differentially expressed genes (DEGs) in F2A is based on Gene Ontology (GO) terms. For each DEG, we retrieved its associated GO annotations across the three main categories (biological process, cellular component, molecular function). In cases where a gene was assigned multiple GO terms, we prioritized the most representative or significantly enriched term for functional interpretation. This clarification has been incorporated into the revised figure legend and the according GO number has been included in the figure.

(3) The rationale for focusing on CHOP is not clear because Ddit3 is not shown in the spatial transcriptomics in F2A and is not significant in F2B, contradicting what is stated in line 206.

We thank the reviewer for raising this important point. We apologize that Ddit3 was missing from the original figure. In the revised manuscript, we have included an updated version of Figure 2A, which now shows that Ddit3 is indeed one of the differentially expressed genes (DEGs) in the Mid zone at both 3 and 6 hours post-APAP. We agree with the reviewer that, as shown in Figure S1G (previous Figure 2B), Ddit3 did not reach statistical significance, due to its relatively low expression level in that analysis. Nevertheless, when we examined transcription factor (TF) activity in the Mid zone during early AILI, Ddit3 and Atf3 ranked as the top two most highly expressed TFs among the top ten with the highest activity, whereas Atf4 ranked seventh (Revised Figure 4B and Figure S3B). Given that Ddit3 frequently co-worked with Atf4 and that the Atf4–Ddit3 axis plays a well-established role in cellular stress adaptation, we considered this pathway to be biologically relevant and worthy of further investigation.

(3) The term "redistribution" used in line 197 to describe the expression of Cyp2e1 and other Cyps in the midlobular zone seems inappropriate, considering that they just continue to be expressed there, whereas pericentral hepatocytes are dying in F3B; the same applies to "Gene Expression Shift" in F3H.

We thank the reviewer for this important clarification. We have revised the text (Revised manuscript, page 9, line 234-236) to state that selective loss of Cyp‑expressing pericentral hepatocytes leads to the mid‑zone becoming the primary site of residual Cyp activity. The figure label has been changed from “Gene Expression Shift” to “Peri‑necrotic Cyp retention” and the legend now explicitly notes that this is an apparent zonal shift due to necrosis, not active redistribution.

Reviewer #3 (Recommendations for the authors):

(1) Please do not use abbreviations like AILI. This makes the paper more difficult to read.

We thank the reviewer for pointing this out. We have replaced AILI with the full term “APAP-induced liver injury” to ensure easiness for readers.

(2) It will be important to clarify how pericentral, mid, and periportal were defined. In Figure 1, it appears that some of the pericentral hepatocytes that are Ki67 positive are quite mid-zonal. It would be important to have rigorous definitions for the location determination.

We thank the reviewer for this constructive comment. To define the pericentral (PC), mid, and periportal (PP) zones, we adopted the classical nine‑layer model of the hepatic lobule described by Lin et al. (PMID: 29618815). Layers 1–2 were designated as the PC zone, layers 3–6 as the mid zone, and layers 7–9 as the PP zone. For quantitative zonal distribution of protein‑positive nuclei (e.g., Ki67, CHOP, ATF4), we calculated a position index (P.I.) based on distances to the nearest central vein (CV) and portal vein (PV), using the law of cosines: P.I. = (x 2 + z 2 – y 2) / (2z 2), where x = distance to CV, y = distance to PV, and z = distance between CV and PV. This quantification method has now been included in the Methods section (Revised manuscript, page 33, line 880-885).

We thank the reviewers for their rigorous critique again. We thank eLife for fostering an environment of fairness and transparency that enables authors to communicate openly and present their data honestly.