Sex-specific KDM6A-HNF4A-CREBH network controls lipoprotein cholesterol metabolism and atherosclerosis via epigenetic reprograming of hepatocytes

Domain: biology
Published: 2026-03-23
Editorial outcome: major_revision

Abstract
The liver is a central organ controlling lipid and cholesterol metabolism and plays a key role in regulating lipoprotein profiles and cardiovascular disease risk. Males and females show clear differences in cholesterol handling and susceptibility to atherosclerosis, but the molecular basis for these sex-specific effects remains incompletely understood. Here we show that the X-linked histone demethylase 6A (KDM6A) is essential for maintaining healthy cholesterol metabolism in the liver. Reducing KDM6A levels in human liver cells disrupts gene programs involved in lipoprotein regulation linked to cardiovascular disorders. Consistently, female mice lacking KDM6A specifically in hepatocytes develops pro-atherogenic blood lipoprotein profiles and increased atherosclerosis under genetic and dietary stress, whereas males are largely unaffected. Mechanistically, KDM6A cooperates with Hepatocyte Nuclear Factor 4 Alpha (HNF4A) to promote chromatin activation and enable CREBH (encoded by CREB3L3)-dependent transcription of lipid metabolic genes. These findings identify KDM6A as a sex-linked regulator of hepatic cholesterol metabolism.

Introduction
The liver plays a key role in cholesterol metabolism. It is the major organ to facilitate cholesterol uptake, biosynthesis, excretion and clearance1. These processes are governed by coordinated transcriptional networks, epigenetically regulated by transcription factors and coregulators2,3. Disturbances of such transcription networks may lead to imbalanced intrahepatic and circulating cholesterol levels and are closely related to human atherosclerotic cardiovascular disease (ACVD)4–6. Progress in the past few decades has revealed key transcription factors (TFs) involved in cholesterol and lipoprotein metabolism in the liver, exemplified by oxysterol-sensing Liver X Receptors (LXRs)7 and bile acid-sensing Farnesoid X Receptor (FXR)8,9, both controlling reverse cholesterol transport, bile acid synthesis and excretion via activating the transcription of a group of cholesterol metabolic genes in the hepatocytes. Other well-studied TFs include fatty acid responsive Peroxisome Proliferator-Activated Receptors (PPARs)10,11 and Sterol Regulatory Element-Binding Protein (SREBPs)12, some of which are targets of therapeutic agents.

In contrast to TFs, the pathophysiological roles of coregulators in liver cholesterol metabolism remain largely unknown. Recent multi-omics approaches have revealed an intrinsic connection between transcriptome and epigenome which is directly linked to coregulators, some of which have enzymatic activities and regulate histone or DNA modifications13–15. Coregulators interplay with TFs and control their transcriptional activity and gene-selectivity by altering the epigenetic chromatin landscape and changing their affinity to TFs and transcriptional activation complexes, amongst many other mechanisms16–18. As coregulators interact with multiple TFs, even subtle changes in their expression may lead to a wide spectrum of physiological outcomes associated with various diseases19,20. Of note, a few functionally very important epigenetic modulators are located in the sex chromosomes21. Some of them can escape X chromosome inactivation and thus become higher expressed in female than in male tissues21,22.

Lysine Demethylase 6A (KDM6A) is among the two key X-linked and X-inactivation ‘escapee’ epigenetic enzymes (the other being KDM5C) 21,22. The C-terminus of KDM6A contains an enzymatic Jumonji C (JmjC) domain which is able to catalyze the removal of di- and trimethylation on H3K27 (H3K27me2/3)22. Because H3K27me2 and 3 are histone markers for tightly packed ‘heterochromatin’, demethylases targeting these two modifications help to transform the chromatin to an active state with more access to transcriptional activation complexes23. Moreover, KDM6A physically interacts with other coregulator complexes such as Mixed-Lineage Leukemia protein 3/4 (MLL3/4, also named Lysine N-methyltransferase 2C/2D or KMT2C/2D) and histone acetyltransferase P300/ CBP, and is involved in enhancer activation via modulating chromatin H3K4 methylation (H3K4me) and H3K27 acetylation (H3K27ac)22. Indeed, many regulatory functions of KDM6A in the cells do not require its demethylase activities but are dependent on its interacting TFs and coregulators22. Because of its important role in shaping chromatin epigenetic landscapes, a number of studies have been conducted in the past years to decipher the physiological function of KDM6A in multiple tissues. So far, most of the studies have been focused on cancer because KDM6A mutations are frequently observed in human cancer patients22,24–26. For example, KDM6A expression was lost in 30% of liver cancer patients in a recent study, and removal of KDM6A promoted liver cancer development24. Similar finding was also reported in earlier studies using enzymatic inhibitors of KDM6A/B in liver cancer cells, which proved the importance of these enzymes in maintaining the liver cell identity and proliferation in vitro27–29. In contrast, the metabolic function of KDM6A is yet largely unexplored. Because KDM6A expression in tissues differs between males and females, whether such difference may contribute to the sex dimorphism in tissue metabolism remains less known. By integrating transcriptomic and epigenomic techniques in human liver cell lines and liver-specific knockout mice and disease models, we plan to investigate the physiological roles and regulatory mechanisms of KDM6A in the liver. We want to address the following questions: i) what the metabolic function of KDM6A in regulating liver lipid metabolism is; ii) how such regulation is modulated in the chromatin epigenetic levels; iii) whether liver KDM6A deficiency leads to sex difference in developing metabolic disorders.

In this study, we discovered that KDM6A knockdown in cell lines derived from females, but not males induced transcriptional changes annotated to lipoprotein and cholesterol metabolic pathways linked with cardiovascular disorders. In accordance, hepatocyte specific Kdm6a knockout (LKO) mice showed unique atherogenic lipoprotein profiles and were prone to developing atherosclerosis upon genetic and dietary challenges, specifically in females but not in males. Cistrome and epigenome analysis revealed that KDM6A is recruited by Hepatic Nuclear Factor 4 Alpha (HNF4A) to the cis-regulatory regions of lipoprotein and cholesterol metabolic genes such as APOA1 (encoding Apolipoprotein A1). This led to subsequent epigenetic changes which enhanced the binding of cholesterol metabolic TFs such as cAMP-responsive element-binding protein H (CREBH, encoded by CREB3L3) to induce the transcription of lipoprotein and cholesterol metabolic genes in hepatocytes. Interestingly, this KDM6A-HNF4A-CREBH network was also observed to control lipoprotein and cholesterol metabolic genes in human liver cell lines from females but not males.

We have therefore identified a cholesterol regulatory mechanism controlled by X-linked epigenetic enzyme KDM6A in the liver. Deciphering the key hubs for the transcription networks of liver cholesterol and lipoprotein metabolism is not only essential for understanding the personalized drug responsiveness in patients but also for identifying novel targets and strategies for intervention.

Results
Hepatocyte-specific depletion of KDM6A triggers lipoprotein dysregulation and atherosclerosis.
To determine the function of KDM6A in the liver cells, we first performed siRNA knockdown of KDM6A in the human liver cell line HROHep03 (derived from a female patient, hereafter referred to as HRO). KDM6A knockdown in the HRO cells led to 195 downregulated and 93 upregulated genes (Fig. 1a). To study whether KDM6A plays a similar role in male liver cells, we surveyed transcriptome of various liver cell lines using public available RNAseq dataset30. We compared the relative KDM6A and KDM6C mRNA levels and their expression ratio with male human primary hepatocytes, and selected Huh1 cells as a human male liver cell model (Supplementary Fig. 1a). Noteworthy, commonly used cell lines such as HepG2 and Huh7 cells had very low/no expression of KDM6C mRNA validated by Quantitative PCR (qPCR) (Supplementary Fig. 1b). Short Tandem Repeat (STR) cell line database confirmed that some of the liver cancer cell lines from males such as Huh7 does not contain Y chromosome. We also tested the karyotypes of HRO and Huh1 cells and found that HRO cells had 2 copies of X chromosomes and Huh1 had 1 copy of X chromosome like Huh7 and HepG2 cells (Supplementary Fig. 1c). Knockdown of KDM6A in the Huh1 cells led to many more downregulated (344) than upregulated (55) genes, similar to HRO cells (Fig. 1b). However, Gene ontology (GO) analysis showed that the downregulated genes were annotated to pathways of cholesterol and lipoprotein metabolism in the HRO cells but not Huh1 cells (Fig. 1c). Representative genes in the KDM6A knockdown HRO cells included APOA1 and APOM, both are components of HDL and involved in their protective roles in atherosclerosis31,32, as well as APOH which was reported to be associated with liver lipid accumulation, steatosis and bile acid regulation33,34 (Fig. 1d). Such cholesterol and lipoprotein metabolic pathways were not enriched in the upregulated genes in both HRO and Huh1 cells (Supplementary Fig. 1d). Of note, HRO and Huh1 cells had very few common KDM6A regulated genes (Supplementary Fig. 1e). This contrasted with data from the Y-absent Huh7 cells. More than 50% of the downregulated and 20% of the upregulated genes in the HRO cells were overlapped with Huh7 cells upon KDM6A knockdown (Supplementary Fig. 1f). Interestingly, common KDM6A target genes between HRO and Huh7 cells were also annotated to lipoprotein and cholesterol metabolic pathways (Supplementary Fig. 1g). Because the X-linked KDM6A and Y-linked KDM6C share similar structures, the two proteins possibly compensated each other’s function in the liver cells. Indeed, several key targets of KDM6A such as ALPOM and APOH could only be reduced by double knockdown of KDM6A and KDM6C in the Huh1 cells, supporting the redundant function of KDM6C and KDM6A on those genes (Supplementary Fig. 1h).

We continued to investigate the in vivo function of KDM6A using LKO mice. Removal of KDM6A surprisingly changed very few genes in the male (13 upregulated versus 13 downregulated) comparing to female (560 downregulated versus 429 upregulated) mice (Supplementary Fig. 1i-1j). The downregulated genes in the male LKO were also quite different from those changed in the female LKO mice (Supplementary Fig. 1k). Interestingly, fast protein liquid chromatography (FPLC) analysis of lipoprotein cholesterol and triglyceride revealed elevation of LDL cholesterol but not triglyceride specifically in chow diet fed female but not male LKO mice (Supplementary Fig. 2a-2d). Lipoprotein and cholesterol metabolic dysregulation are tightly connected to cardiovascular diseases such as atherosclerosis. To study the function of KDM6A in atherosclerosis, we created an adeno-associated virus (AAV)-driven disease model. The AAV expresses the mouse constitutively active PCSK9 (harboring a variant encoding p.D377Y) driven by the liver-specific Thyroxine Binding Globulin (Tbg) promoter. The mice were then fed with high fat and high cholesterol diet (western diet-WD) for 12 weeks35 (hereafter referred to as AAV-PCSK9). The plasma FPLC analysis showed that KDM6A deletion in the liver induced an over 2-fold increase of VLDL cholesterol and a more than 70% reduction of HDL cholesterol without affecting the lipoprotein triglyceride levels (Fig. 1e-1f). While the VLDL triglyceride level remained unchanged (Fig. 1g-1h). Again, such changes were only observed in female but not in male mice (Supplementary Fig. 2e-2h). In order to identify the mechanisms of the increased VLDL cholesterol, we performed a VLDL production assay in the female AAV-PCSK9 WT and LKO mice by inhibiting the lipase activity in the mice with poloxamer-407 (P-407) and measuring the VLDL triglyceride and cholesterol at different time points by FPLC (Fig. 1i-1j). We found that VLDL triglyceride production was slightly but not significantly decreased, while VLDL cholesterol level increased by 2 folds (Fig. 1i-1j). Analysis of the VLDL fraction revealed a significantly higher cholesterol-to-triglyceride ratio in LKO mice compared to WT controls (Supplementary Fig. 2i), indicating an altered VLDL particle composition in the LKO mice. While total plasma APOB100 levels were elevated in female LKO mice during the VLDL production assay (Supplementary Fig. 2j), these levels remained uncoupled from the progressive rise in VLDL triglycerides and cholesterol following lipase inhibition by P-407 (Fig. 1i-1j). Because both APOB100 and APOB48 can be structural components of murine VLDL particles, APOB100 alone cannot reflect the VLDL difference between the WT and LKO mice. To precisely determine VLDL particle numbers, we employed Single-Particle Profiling (SPP)36. SPP is a state-of-the-art high-resolution biophysical technique which analyses fluorescently stained purified VLDL using a specific chemical called NR12S37. By monitoring the continuous emission signal fluctuation from multiple fluorescent channels, the technique was able to directly quantify the VLDL particle numbers in the WT and LKO mice serum. The results demonstrated that relative VLDL particle numbers were similar between WT and LKO mice (Fig. 1k), supporting that the observed hypercholesterolemia in LKO mice was driven by cholesterol-enrichment of VLDL particles rather than an increase in particle secretion. Interestingly, the intrahepatic total and free cholesterol levels increased by more than 30% while the liver triglyceride level was significantly lower in the LKO female mice (Fig. 1l-1m). In contrast, the male WT and LKO AAV-PCSK9 mice did not show difference (Supplementary Fig. 2k-2l). To investigate the transcriptomic regulation by KDM6A in the liver, we performed RNAseq in the WT and LKO AAV-PCSK9 mice. The result showed approximately identical down and upregulated gene number (Fig. 1n). The downregulated genes in both untreated and AAV-PCSK9 mice were annotated to multiple lipoprotein metabolic pathways (Fig. 1o). In particular, we have observed reduced expression of genes in lipid and cholesterol metabolic pathways in the AAV-PCSK9 mice (Fig. 1p-1q). For example, reduced bile acid synthesis genes such as Cyp7a1, Cyp8b1 and Akr1d1 might attenuate liver cholesterol clearance via bile acids and lead to accumulated cholesterol in the liver (Fig. 1q), while downregulation of bile acid process and excretion genes like Baat, etc, might further sequest bile acids in the liver which was observed in the AAV-PCSK9 female LKO mice (Supplementary Fig. 2m-2n). In addition, total bile acid levels were almost 10 times higher in the LKO females but not male mice (Supplementary Fig. 2o). Bile acid composition analysis in the mouse serum showed that the unconjugated cholic acid (CA), deoxycholic acid (DCA), chenodeoxycholic acid (CDCA) and ursodeoxycholic acid (UDCA) were not different, but the conjugated bile acids such as taurine-CA (TCA), TDCA, TCDCA and TUDCA were more than 10 times higher in the LKO female mice (Supplementary Fig. 2p-2q). Moreover, the rodent-specific unconjugated muricholic acids (MCA) were not changed, but the conjugated T-MCAs were also more than 10 times higher in the female LKO mice serum (Supplementary Fig. 2r-2s).

To further investigate the impact of KDM6A loss on bile acid homeostasis, we measured plasma levels of 7α-hydroxy-4-cholesten-3-one (C4), a commonly utilized surrogate marker for hepatic bile acid synthesis. Surprisingly, despite the downregulation of the rate-limiting enzyme CYP7A1, female LKO mice exhibited significantly elevated plasma C4 levels (Supplementary Fig. 2t). This elevation likely reflected a complex metabolic uncoupling common in cholestatic liver injury. CYP8B1 is a key enzyme which converts C4 into bile acids. The reduction of this enzyme in the LKO mice might create a metabolic bottleneck, leading to the accumulation and subsequent spillover of C4 into the systemic circulation despite a potential reduction in de novo biosynthesis. Therefore, while the C4 levels are elevated, the marked accumulation of conjugated bile acids and the suppression of transcriptional synthesis markers collectively pointed toward a cholestatic phenotype with impaired bile acid excretion.

These data strongly suggest the imbalanced liver cholesterol metabolism caused by defect bile acid excretion, leading to the conjugated bile acids and intermediates to spill over into the blood. In addition, the transporters SLCO1B2 and NTCP which control bile acid reuptake and were also significantly lower in the LKO mice, impairing the transport of bile acids back to the liver. The reduced liver triglyceide might be caused by lower expression of lipogenesis genes such as Srebp and Dgat2 in the female LKO mice. Some of the genes were tested in the protein level by western blot (Supplementary Fig. 2u). Collectively, Kdm6a knockout in the female liver altered multiple cholesteorl and lipid metabolic pathways which changed the intrahepatic triglyceride and cholesterol levels. This might in turn affected the liver-derived VLDL triglyceride and cholesterol composition in the AAV-PCSK9 mice. Not surprisingly, the elevated atherogenic lipoprotein profile was paralleled with a more severe atherosclerosis phenotype in the LKO female but not male mice, as shown by the aortic root oil red staining and quantification in the AAV-PCSK9 WT and LKO mice (Fig. 1r-1t). Using a public type 2 diabetes (T2D) knowledge portal38, we explored the association of the rare coding variants in KDM6A with cardiovascular disease traits. We found that KDM6A variants correlated positively with apoA1 and negatively with apoB levels (Supplementary Fig 2v), consistent with what was discovered in the LKO mice (Fig. 1e-1h, Supplementary Fig. 2a-2d).

KDM6A regulation of lipoprotein metabolic genes is independent of H3K27me3 demethylation.
KDM6A controls histone H3K27me3 demethylation through its demethylase activities22. It also works with other coregulator complexes such as MLL3/4 and CBP to modulate histone H3K4me1/2 and H3K27ac, independent of its enzymatic functions22. To decipher the epigenetic mechanisms underlying the lipoprotein regulation by KDM6A, we performed chromatin immunoprecipitation sequencing (ChIP-seq) of H3K4me1/2, H3K27ac and H3K27me3 in control and siKDM6A transfected HRO and Huh1 cells. The coverage plots showed that KDM6A knockdown did not change global H3K4me1/2 or H3K27ac, with minor or no effects to increase genome-wide histone H3K27me3 in both HRO and Huh1 cells (Fig. 2a-2h). Similarly, in the downregulated genes, HRO and Huh1 cells also had very minor overall changes at H3K4me1/2 (Huh1) or H3K27ac (Huh1 and HRO) but not H3K27me3 (Supplementary Fig. 3a-3h). However, when we performed correlation analysis by comparing H3K4me1/2, H3K27ac and H3K27me3 epigenetics changes with siKDM6A altered transcriptomics at significantly regulated gene loci (padj<0.05) in both HRO and Huh1 cells, we observed significant (although weak) correlations of H3K4me1/2 and H3K27ac but not H3K27me3 changes with mRNA expression (Fig. 2i-2p), Suggesting the transcriptomic regulation by KDM6A did not rely on H3K27me3 in both HRO and Huh1 cells.

We then asked whether KDM6A regulation on its target lipoprotein and cholesterol metabolic genes in HRO cells was also independent of H3K27me3. To study this, we checked all the genes annotated to lipoprotein and cholesterol metabolic pathways, as well as cardiovascular related diseases in the GO and DO analysis, and identified 135 genes in total, hereafter referred to as lipoprotein and cholesterol (LnC) metabolic genes (Supplementary Fig. 3i). We then performed a correlation analysis to compare the H3K4me1/2, H3K27ac and H3K27me3 with the mRNA expression changes specifically at the LnC gene loci. While there was no significant correlation of H3K27me3 with the LnC genes, H3K4me1/2 and H3K27ac were all significantly associated with LnC mRNA expression (Supplementary Fig. 3j-3m). This was consistent with epigenetic changes at APOA1/APOC3 gene cluster, which showed decreased H3K4me1/2 and H3K27ac, but not H3K27me3 upon KDM6A knockdown in the HRO cells (Fig. 2q). Similar findings were observed at APOM and APOH loci, further supporting that H3K27me3 was not involved in suppressing the LnC gene expression upon KDM6A knockdown in the HRO cells (Supplementary Fig. 3n-3o). It is not surprising that no epigenetic markers were altered at APOA1/APOC3, APOM or APOH were not regulated by KDM6A at Huh1 cells as none of the genes were downregulated upon KDM6A knockdown in the Huh1 cells (Fig. 2r, Supplementary Fig. 3p-3q). However, at Huh1-specific KDM6A target gene loci exemplified by SLC6A19, similar reduction of H3K4me1/2 and H3K27ac was observed, while H3K27me3 maintained the same (Fig. 2s). In consistence, knocking down of MLL3/4 (or KMT2C/2D) had similar effects on LnC genes such as APOA1, APOM and APOH in the HRO cells (Fig. 2t).

We also performed H3K4me1/2, H3K27ac and H3K27me3 ChIP-seq in the female floxed and LKO mouse liver samples. In contrast to the HRO cells, there was a slight decrease in H3K4me1 both genome-widely and at downregulated gene loci (Supplementary Fig. 3r-3s). The H3K27me3 showed a marginal global increase in the female LKO liver but not at downregulated gene loci (Supplementary Fig. 3r-3s), while H3K4me2 and H3K27ac were not noticeably changed (Supplementary Fig. 3r-3s). At Apoa1/Apoc3/Apoa4 gene cluster, the H3K4me1/2 and H3K27ac were decreased, but H3K27me3 was not changed (Supplementary Fig. 3t) At the Apom locus, H3K4me1 and H3K27ac were decreased while H3K27me3 remained consistently unchanged (Supplementary Fig. 3u).

Collectively, our finding suggested that the KDM6A regulation on both global transcriptome and LnC genes in the HRO cells was independent of its H3K27me3 demethylase activities but rather require its non-enzymatic function via interplaying with complexes such as MLL3/4.

HNF4A is required to recruit KDM6A to lipoprotein genes and regulates their transcription.
KDM6A binding at HRO and Huh1 cells appeared largely overlapped (Supplementary Fig. 4a). To identify the TFs responsible for KDM6A recruitment to the LnC genes, we first did a motif analysis in KDM6A CUT&Tag peaks in HRO cells. Such analysis has identified multiple TF motifs at KDM6A binding sites genome widely (Fig. 3a). KDM6A CUT&Tag peaks in Huh1 cells were also enriched with identical top motifs as in HRO cells (Supplementary Fig. 4b), with NF4A-DR1 as common motifs with HepG2 KDM6A ChIP-seq peaks (Supplementary Fig. 4c). To further narrow down the TF candidates, we performed a rapid immunoprecipitation mass spectrometry (RIME)39, in which we immunoprecipitated KDM6A in the chromatin of Huh7 nuclei for proteomics analysis. The RIME experiment unbiasedly identified multiple TFs interacting with KDM6A in the chromatin level (Fig. 3b). Integrating the motif analysis with the RIME results identified HNF4A as a key transcription factor. We then compared existing HNF4A ChIP-seq 40 with the KDM6A ChIP-seq40 results in the HepG2 cells, and found that more than 90% of KDM6A peaks were colocalized with HNF4A (Supplementary Fig. 4d).

To further demonstrate that HNF4A is required for KDM6A binding in the human liver cells, we performed CUT&Tag analysis of KDM6A in control versus HNF4A knockdown HRO and Huh1 cells. KDM6A was also genome-widely decreased upon HNF4A knockdown in HRO cells (Fig. 3c-3d). There were 8546 KDM6A peaks decreased upon HNF4A knockdown in the HRO cells (Fig. 3e). These peaks are annotated to LnC genes such as APOA1/4, APOM and APOH, among many others (Fig. 3e). To be noted, at APOA1/APOC3 and APOA5 and APOC2/4 loci which had both KDM6A and HNF4A peaks. When HNF4A was removed, the KDM6A binding almost disappeared at these gene locus (Fig. 3f, Supplementary Fig. 4e-4f). In contrast, CLPTM1 gene promoter only had KDM6A peak without HNF4A, and knockdown of HNF4A did not affect KDM6A at this locus (Supplementary Fig. 4f).

In contrast, knockdown of HNF4A in Huh1 cells did not change global KDM6A binding (Supplementary Fig. 4g-4i). However, at some LnC gene loci such as APOA1/APOC3 cluster, siHNF4A released KDM6A binding in the Huh1 cells (Supplementary Fig. 4j). This seemingly contradicted result might be explained by coregulation of such LnC genes by both X-lined KDM6A and Y-linked KDM6C. As both enzymes share similar transcription factor binding domains, they are likely to be recruited by HNF4A at APOA1/APOC3 loci. Loss of HNF4A therefore resembled KDM6A and KDM6C double knockdown at those genes, leading to reduced expression of APOA1/APOC3 and released KDM6A at the same time (Supplementary Fig. 1h), while KDM6A knock down alone did not change APOA1/APOC3 mRNA in the Huh1 cells (Fig. 1d).

The coregulation of LnC genes by KDM6A and HNF4A was further evaluated by comparing the transcriptomic changes in KDM6A and HNF4A knockdown HRO cells separately. Despite that there were only 80 commonly regulated genes in comparison to 1368 and 206 HNF4A and KDM6A-specific genes, there was a significant correlation between mRNA changes siKDM6A and siHNF4A HRO cells (Fig. 3g, Supplementary Fig. 4k). LnC genes such as APOA1 and APOM were targets of both HNF4A and KDM6A (Fig. 3g). GO analysis in the KDM6A/HNF4A coregulated as well as KDM6A or HNF4A specific genes demonstrated that the commonly downregulated genes upon KDM6A and HNF4A knockdown were annotated to multiple lipoprotein regulatory pathways (Fig. 3h). The dependency of KDM6A on HNF4A in regulating key LnC genes such as APOA1, APOM and APOH was further proved by double knocking down of KDM6A and HNF4A in the HRO cells, which showed no further decrease of these genes comparing with the siHNF4A group alone (Fig. 3i-3j).

We also did similar comparison in Huh1 cells. Despite that KDM6A had very different gene targets in Huh1 cells, with very few lipoprotein and cholesterol metabolic genes, knockdown of KDM6A had a significant correlation with siHNF4A in mRNA expression changes (Supplementary Fig. 4l). The KDM6A and HNF4A commonly regulated genes were not annotated to any cholesterol or lipoprotein metabolic pathways (Supplementary Fig. 4m and 4n). Interestingly, KDM6A was significantly released by HNF4A knockdown in KDM6A/HNF4A common genes in the HRO cells (Supplementary Fig. 4o) but not in Huh1 cells (Supplementary Fig. 4p).

These above data collectively showed that HNF4A and KDM6A interact in the chromatin level to control gene expression in both HRO and Huh1 cells. However, the common target genes were sex specific and mostly annotated to lipoprotein and cholesterol metabolic pathways in the HRO liver cells from female patients.

The interplay of HNF4A and KDM6A regulates epigenetic changes in the liver cells
We asked whether the chromatin interplay of HNF4A and KDM6A regulated epigenetic changes in the liver cells. Knockdown of HNF4A in the HRO cells had minor changes of H3K4me1 and H3K27me3, and identical H3K4me2 and H3K27ac levels (Fig. 4a-4d). While in Huh1 cells, siHNF4A drastically reduced H3K27me3 without changing H3K4me1/2 or H3K27ac (Supplementary Fig. 5a-5d), suggesting HNF4A might interact with other epigenetic enzymes such as histone methyl-transferases. However, H3K4me1 (Fig. 4e), H3K4me2 (Fig. 4f), H3K27ac (Fig. 4g) and H3K27me3 (Fig. 4h) epigenetic alterations in HNF4A and KDM6A knockdown HRO cells showed significant correlations, similar to observations in Huh1 cells (Supplementary Fig. 5e-5h), supporting the concept of coregulation of KDM6A and HNF4A in the control of global epigenetic landscapes. We also analyzed the same epigenetic markers specifically at the HNF4A dependent KDM6A binding sites, the KDM6A peaks which were downregulated upon HNF4A knockdown (Fig. 3e). Only H3K4me1 (Fig. 4i), but not H3K4me2, H3K27ac or H3K27me3 (Fig. 4j-4l), was decreased upon HNF4A siRNA transfection in the HRO cells. At APOA1, APOH and APOC2/APOC4 gene clusters, H3K4me1 and H3K27ac or H3K4me2 were consistently downregulated, while H3K27me3 remained unchanged in these gene loci (Fig. 4m-4o). Interestingly, in the Huh1 cells, knockdown of HNF4A at HNF4A-dependent KDM6A peaks led to reduction of all the epigenetic markers including H3K4me1/2, H3K27ac and H3K27me3 (Supplementary Fig. 5i-5l), indirectly supporting that KDM6A loss might be related with the H3K4me1/2 and H3K27ac changes. We have also seen reduced H3Kme1/2 or H3K27ac levels at APOA1, APOH and APOC2/APOC4 clusters (Supplementary Fig. 5m-5o), consistent with KDM6A (and maybe also KDM6C) loss at those loci (Supplementary Fig. 4j).

KDM6A removal affects the binding and transcription activation of CREBH
We hypothesized that KDM6A/HNF4A-modulated changes of the histone modifications H3K4me1/2 and H3K27ac could affect the chromatin accessibility of TFs at key LnC genes. To identify these TFs, we first performed the TF enrichment analysis using CHEA341, which takes into account TF–gene co-expression from RNA-seq studies, TF–target associations from ChIP-seq experiments, and TF–gene co-occurrence computed from crowd-submitted gene lists. Using the coregulated gene list by KDM6A and HNF4A in the RNA-seq data from HRO and Huh1 cells, we have identified very different common TFs between HRO and Huh1 cells (Fig. 5a, Supplementary Fig. 6a). The common top candidates in HRO cells include CREBH and NR1H4 (FXR), both are key regulators of lipid and cholesterol in the liver. While in Huh1 cells, the enriched top listed TFs were a group of Zinc Finger Proteins (ZNF) family TFs such as ZNF692, etc (Supplementary Fig. 6a). This was not unexpected considering the major transcriptomic difference between HRO and Huh1 cells (Supplementary Fig. 1e) and that there were very few lipid or cholesterol metabolic genes in HNF4A/KDM6A commonly regulated genes (Supplementary Fig. 4m-4n). We then tested the response of both mice and HRO cells to FXR agonists GSK2324 and GW4064 upon KDM6A removal. However, most of the genes annotated in the NR1H4 targets (including the lipoprotein modulating APOA1, APOM or APOH) were not induced by FXR agonists, neither in mice (Supplementary Fig. 6b) nor in HRO cells (Supplementary Fig. 6c). Among the very few genes which were stimulated by FXR agonists, Small Heterodimer Partner (SHP) expression was decreased by KDM6A deletion in both mice and HRO cells (Supplementary Fig. 6b-6c). However, SHP is a corepressor which inhibits LXR activities and bile acid synthesis, which leads to cholesterol accumulation in the liver42. The reduced SHP in the liver therefore was less likely to contribute to the higher intrahepatic cholesterol and blood LDL cholesterol levels in the LKO mice. In mice, the bile acid export pump Bsep expression was reduced by KDM6A knockout, but its alteration was marginal (Supplementary Fig. 6b). We also tested LXR target genes in the mice and HRO cells. Although LXR was not identified in the TF enrichment analysis, analysis by qPCR in the AAV-PCSK9 injected mice showed reduced mRNAs for Cyp7a1 and Cyp8b1, both LXR-regulated rate-limiting enzymes in bile acid synthesis (Fig. 1q). However, treatment with the LXR agonists GW3965 and T0901317 did not induce expression of either gene in the mouse hepatocytes or HRO cells (Supplementary Fig. 6d-6e), despite that knockout of KDM6A showed reduced expression of Cyp7a1 and Cyp8b1 already in the untreated mouse hepatocytes (Supplementary Fig. 6d). Based on these results, we conclude that the FXR or LXR are not the main regulators directly affected by KDM6A.

We therefore continued to study CREBH from the top TF candidate list. We found that knockdown of CREBH in the HRO cells decreased the expression of key lipoprotein genes such as APOA1, APOC2, APOH and APOM (Fig. 5b), similar to the changes observed in KDM6A knockdown. We also performed ChIP-seq in the control and KDM6A knockdown HRO cells. At the APOA1 and APOC2/APOC4 gene clusters, CREBH binding was reduced by 62% and 56% after KDM6A removal (Fig. 5c and 5d). The changes at APOH and APOM were not conclusive because the CREBH peaks at these two gene loci were not clear due to relatively low quality CREBH antibodies. While in KDM6A absent gene loci, such as XR_001752718.3 and CHRNE, CREBH binding was not affected in the knockdown group, supporting the validity of the results (Supplementary Fig. 6f-6g). The genome-wide recruitment of CREBH was not changed upon KDM6A knockdown in the HRO cells (Fig. 5e), but CREBH binding at KDM6A target loci (KDM6A regulated genes with KDM6A binding) was significantly reduced upon KDM6A knockdown (Fig. 5f).

We also performed similar analysis in the female AAV-PCSK9 mice. The TF enrichment analysis also identified CREBH and NR1H4 in the top list (Fig. 5g). We also managed to perform CREBH ChIP-seq in mouse liver for the first time. The results showed reduction of CREBH peaks at KDM6A target genes in the mice, such as Apoa1 (Fig. 5h), Apoa5, Cyp7a1, Cyp8b1, Lcat, Srebf1 and Dgat2 (Supplementary Fig. 6h-6m). While in control gene Nlrp9b which was not regulated by Kdm6a knockout, CREBH binding was not affected (Supplementary Fig. 6n). We have also found that the CREBH binding in the mouse liver was decreased in the LKO mice both genome-widely (Fig. 5i) and at downregulated gene loci (Fig. 5j) in the AAV-PCSK9 LKO mice.

Based on these results, we propose the model that KDM6A interplays with HNF4A to control the H3K4me1/2 and H3K27ac at lipoprotein and cholesterol metabolic gene loci in the liver cells. These epigenetic changes were linked with chromatin accessibility of CREBH to such loci. As a result, liver deletion of KDM6A affected the binding of CREBH which subsequently reduced the expression of lipoprotein and cholesterol metabolic genes such as APOA1, APOM and APOH. These in combination contributed to the altered lipoprotein cholesterol levels and promoted atherosclerosis in the LKO mice (Fig. 5k).

Methods
Ethics. All animal experiments were approved by Swedish Board of Agriculture with ethic permit number 05517-2022 and ID907. The experiments were conducted in accordance with the International Guiding Principles for Biomedical Research Involving Animals, as developed by the Council for International Organizations of Medical Sciences (CIOMS). The mice were bred and maintained at the Center for Comparative Medicine at Karolinska Institutet and University Hospital (PKL, Huddinge, Sweden).

Mice. Kdm6aflox/flox mice were developed in Cyagen using a targeting construct which contains loxp sites flanking exon 5 of Kdm6a. To create the LKO mice, the Kdm6aflox/flox mice were crossed with Alb-Cre mice (B6.Cg-Speer6-ps1Tg(Alb-cre)21Mgn/J) obtained from Jackson Laboratory (stock no. 003574). Both Kdm6a flox/flox and the Alb-Cre mice were bred with wild type C57BJ6 mice for at least 9 generations before breeding. The paired Kdm6aflox/floLxAlb-Cre-/- mice were used as negative controls. The mice are kept in IVC-cages with a house, nesting materials and gnawing stick. The cages used are Tecniplast GM500 (Greenline) : 391 x 199 x 160 (172*) mm (501 cm2) at PKL4 and Allentown typ II Long V12, : 213 x 362 x 175 (127) mm (542 cm2) or Allentown NexGen: 194 x 178 x 397 (535 cm2) at PKL3 and 5. Max 5 animals in one cage. The temperature are 22 ± 2 degrees and the humidity 50 ± 5. The light cycle is 12/12.

Atherosclerosis model.
To generate the atherosclerosis mouse models, twelve-week-old control (WT) and LKO mice received a single intraperitoneal injection of 1011 particles of rAAV8/D377Y-mPCSK9 per mouse. The mice were then fed a western diet which contains 40% fat, 0.5% cholesterol (D12107C, Research Diets) for 12 weeks. Body weight and blood glucose levels were measured at specified time points using an electronic weighing scale and a glucometer (Accu-Chek Performa, Roche).

Atherosclerosis quantification35 was performed at the end of the treatment period, mice were euthanized using CO₂ asphyxiation followed by cervical dislocation. The aorta was carefully dissected from the aortic root to the iliac bifurcation, and surrounding adipose tissue was removed. The aortic root was embedded in optimum cutting temperature (OCT) compound and stored at -80°C until cryosectioning. Sections were stained with Oil Red O to visualize lipid deposits and counterstained with hematoxylin. Stained sections were examined under a light microscope, and images were captured using a digital camera. The extent of atherosclerotic lesions was quantified using ImageJ software.

Lipid and cholesterol analysis.
Plasma lipoproteins were fractionated from 2.5 μl of plasma samples from each mouse using a Superose 6 INCREASE 3.2/30 column (GE Healthcare), followed by online determination of triglycerides and cholesterol levels66. Liver TG concentration was determined using a Triglyceride Quantification Kit (MAK266, Merck), and liver cholesterol concentration was determined using a Cholesterol Quantification Assay Kit (CS0005, Merck) according to the protocols. Liver, serum and feces total bile acid concentration was determined using a Mouse Total Bile Acids Assay Kit (80471, Crystal Chem) according to the protocol. Mouse plasma APOB100 was measured using an ELISA kit (ab230932, abcam) according to the protocol. C4 and bile acids in serum were analyzed by LC-MS/MS using deuterium-labeled standards of unconjugated and taurine-conjugated BAs using protocols described previously67,68. Deuterium-labeled standards of unconjugated and glycine- and taurine-conjugated BAs were from Steraloids, Inc. (Newport, RI).

Bile acids and C4 analysis.
Individual bile acids in serum were quantified by liquid chromatography–tandem mass spectrometry (LC–MS/MS) using deuterium-labelled internal standards for unconjugated, glycine- and taurine-conjugated bile acids, their sulfate metabolites, and the bile acid synthesis marker C4, as previously described. Deuterium-labelled standards were obtained from Steraloids, Inc. (Newport, RI, USA). Data were acquired on a Xevo TQ-XS triple quadrupole mass spectrometer (Waters Corp., MA, USA) coupled to an Acquity UPLC system and processed using MassLynx software (Waters Corp., MA, USA). A total of 10 serum samples from mice were analyzed, comprising 2 experimental groups with 5 mice in each group. Each biological sample was prepared and injected once (n=1 technical replicate), alongside pooled QC samples and blank injections to assess carry-over and analytical variability. 50 μL serum were spiked with 10 μL of each deuterium-labelled C4 and bile acid internal standard solution (10 ng d7-C4 and 25 ng d4-bile acid), mixed with 400 μL acetonitrile and vortexed for 5 s. After centrifugation for 15 min at 14 000 g, supernatants were transferred to glass tubes and evaporated to dryness under a stream of nitrogen at 40 °C. Residues were reconstituted in 200 μL methanol–water (1:1, v/v), and 5 μL were injected for LC–MS/MS analysis. HPLC-grade water, methanol (LC–MS grade), acetonitrile, and isopropanol were used together with formic acid as modifiers. Chromatography was performed on an Acquity UPLC BEH C18 column (1.7 μm, 2.1 × 50 mm; Waters Corp., MA, USA) maintained at 65 °C with a flow rate of 0.5 mL/min and an injection volume of 5 μL. The autosampler was kept at 10 °C. Mobile phase A consisted of water with 0.1% formic acid, and mobile phase B consisted of acetonitrile with 0.1% formic acid. The gradient (total run time 22 min) started at 95% A/5% B, was held to 0.5 min, then changed to 75% A/25% B at 5.0 min, 60% A/40% B at 10.5 min, and 5% A/95% B at 17.5–19.0 min, before returning to 95% A/5% B at 19.5–22.0 min for column re-equilibration. Weak and strong needle wash solutions were 10% methanol in water and isopropanol:methanol:acetonitrile:water (25:25:25:25, v/v/v/v), respectively. Analyses were performed on a Xevo TQ-XS triple quadrupole mass spectrometer (Waters Corp., MA, USA) equipped with an electrospray ionization source operated in positive ion mode for C4 and in negative ion mode for bile acids. The vaporizer temperature was 350 °C, spray voltage 3.5 kV, desolvation gas flow 1000 L/h, cone gas 150 L/h, and nebuliser gas 7 bar. Data were acquired in multiple reaction monitoring (MRM) mode using optimized precursor/product ion transitions and compound-specific cone voltages and collision energies for each analyte and its corresponding deuterium-labelled internal standard.

The following bile acids and related compounds were quantified together with their deuterium-labelled internal standards: C4/C4-d7, CA/CLA-d4, CDCA/CDCA-d4, DCA/DCA-d4, GCA/GCA-d4, GCDCA/GCDCA-d4, GDCA/GDCA-d4, GLCA/GLCA-d4, GUDCA/GUDCA-d4, LCA/LCA-d4, TCA/TCA-d4, TCDCA/TCDCTA-d4, TDCA/TDCA-d4, TLCA/TLCA-d4, TUDCA/TUDCA-d4, UDCA/UDCA-d4, α-MCA/α-MCA-d4, β-MCA/β-MCA-d4, ω-MCA/ω-MCA-d4, Tα-MCA/Tα-MCA-d4, Tβ-MCA/Tβ-MCA-d4, and Tω-MCA/Tω-MCA-d4.

Peak integration and quantification were performed in MassLynx, using calibration curves constructed from authentic standards with internal-standard normalization. Analyte identity was confirmed by matching retention times and MRM transitions to standards; QC samples were used to monitor precision and stability over the batch.

VLDL production assay.
Hepatic VLDL secretion assay1 was performed before the end of the treatment period, mice were fasted for 5 hours prior to the experiment. Following the fasting period, baseline blood (~75 µL) was collected. Immediately afterwards, mice received an intraperitoneal injection of poloxamer-407 (16758, Sigma) at a dose of 1.0 g/kg body weight. The P-407 solution was prepared as a 10% (w/v) suspension in saline. Subsequent blood samples (~75 µL each) were collected at 1, 2, and 5 hours after P-407 administration. Plasma lipoprotein levels were measured using FPLC as mentioned above.

VLDL isolation.
Plasma samples (100 µL each) were supplemented with 900 µL of KBr solution (1.019 g/mL) and their densities were adjusted to 1.019 g/mL using KBr. The samples were transferred to 1 mL thick-wall Polycarbonate tubes (Backman Coulter, USA). The tubes were ultracentrifuged for 20 hours at 244000xg at 4°C (Optima MAX-TL Ultracentrifuge, rotor TLA 120.2, Beckman Coulter, USA). Post-centrifugation, the top VLDL-rich fractions were transferred into the new tubes for further refinement. For the second ultracentrifugation step, the VLDL-rich fractions were further brought to the volume of 1 mL using KBr solution (1.019 g/mL) and adjusted to 1.019 g/mL. The ultracentrifugation step was repeated and top VLDL-rich fractions collected. These fractions were further concentrated using Amicon Ultra centrifugal concentrators (0.5 mL, 3 kDa cut-off). This step was repeated twice to replace KBr with PBS. The size of particles present in the VLDL fractions was assessed by Dynamic Light Scattering using Zetasizer (Malvern). Furthermore, 20 µL of each VLDL fraction were transferred to the epi and labelled with 0.5 µL of NR12S37 (0.1 mg/mL in DMSO).

Single Particle Profiling
Single Particle Profiling (SPP) was performed using the setup for fluorescence correlation spectroscopy on a Zeiss LSM 780 microscope36. A 488 nm argon ion laser was used for NR12S excitation69. A 40×1.2 NA water immersion objective was used to focus the light. The laser power was set to 1% of the total laser power that corresponds to 20 μW. The emission detection windows on the single photon counting GaAsP detector were set to 490–560 for green and 650–700 for red parts of the spectrum. Emission from NR12S was recorded simultaneously in both channels. Forty intensity traces of 15 s were recorded. Traces were then analysed using the python-based software “Py Profiler”.

Cell culture studies. Huh7 (RRID: CVCL_0336, Cytion-300156), SNU-182 (RRID: CVCL_0090, Cytion-305119), Huh-1 (RRID: CVCL_2956, JCRB- JCRB0199, established by Hoh,H.) and HROHep03 (RRID: CVCL_2U72, Cytion-300197) cell lines were tested for mycoplasma contamination before the experiments. Huh7 and Huh-1 cells were cultured in Dulbecco’s Modified Eagle Medium (DMEM, D5648, Merck) supplemented with 10% fetal bovine serum (FBS, SH30071.03, HyClone) and 1% Penicillin-Streptomycin (P4458, Merck). SNU-182 cells were cultured in RPMI 1640 (A10491-01, Gibco) supplemented with 10% fetal bovine serum (FBS, SH30071.03, HyClone) and 1% Penicillin-Streptomycin (P4458, Merck). HROHep03 cells were maintained in high-glucose DMEM/F12 medium (SLM-243-B, Merck), supplemented with 5% FBS (SH30071.03, HyClone) and 1% Penicillin-Streptomycin (P4458, Merck). Both cell lines were incubated at 37°C in an atmosphere of 5% CO2.

Karyotyping. Genomic DNA was extracted from the cell lines HROhep03, Huh-1, Huh7, SNU-182, and HepG2 using the Blood & Cell Culture DNA Mini Kit (13323, Qiagen). Male primary human aortic smooth muscle cells (ATCC, PCS-100-012) were used as a diploid reference control. Quantitative PCR (qPCR) was performed with equal amounts of DNA from each cell line to target the Amelogenin X-Linked (AMELX) gene (Forward: 5’ GCTTGCCTCTGCTGAAATATTAGTG3’, Reverse: 5’CTCATGCATTCCGCTGTTCTG3’. The copy number of the X chromosome in each cell line was calculated relative to the HASMC control using the formula: X chromosome copy number = 2^(ΔCT), where ΔCT = CT (PASMC) - CT (cell line).

siRNA transfection. Cells were seeded in 6-well plates at a density of 7.5 × 105 cells/well and incubated for 12 hours in their respective culture media to achieve 60-80% confluency at the time of transfection. siRNA targeting KDM6A, HNF4A, KMT2C, KMT2D and CREBH, as well as negative control siRNA, 10 nmol siRNA was used for each well. Transfections were performed using Lipofectamine RNAiMAX Transfection Reagent (ThermoFisher) following the manufacturer's protocol. Briefly, Lipofectamine RNAiMAX Reagent and siRNA were each diluted in Opti-MEM® Medium and then combined at a 1:1 ratio. The siRNA-lipid complexes were allowed to form by incubating the mixture for 5 minutes at room temperature. The complexes were then added to the cells, and the transfection was carried out for 48 hours at 37°C. Subsequent experiments were performed following the transfection period. The siRNA sequences were listed in Supplementary Table 1.

qPCR analysis. Total RNA was extracted from snap-frozen liver tissues and human cell lines using the E.Z.N.A.® Total RNA Kit I (R6834-02, Omega Bio-tek). Complementary DNA (cDNA) synthesis was performed using M-MLV Reverse Transcriptase (28025-021, Life Technologies). qPCR was conducted on the ABI Prism 7500 PCR system (Applied Biosystems). For normalization, 36b4 (acidic ribosomal phosphoprotein P0, also known as Rplp0) was used as the house keeping gene in mouse liver, while Ubiquitin C (UBC) and Hydroxymethylbilane Synthase (HMBS) were used as house keeping genes for human cell lines. Relative changes in mRNA expression were calculated using the comparative cycle threshold method (2−ΔΔCt). Primer sequences are listed in Supplementary Table 2.

RNA-seq. RNA was extracted from mouse liver biopsies or human cell lines using the kit stated above. RNA quality was assessed using the 5400 Fragment Analyzer System (Agilent). mRNA library preparation was carried out using the Novogene NGS RNA Library Prep Set (PT042, Novovene) following the manufacturer’s protocol. RNA-seq libraries were sequenced on the NovaSeq X Plus Series (PE150) platform at NOVOGENE (Cambridge, United Kingdom). Preprocessed reads were aligned to the NCBI38/mm10 (for mouse) or GRCh38/hg38 (for human) genomes using the HISAT2 program, and read counts were determined using featureCounts v1.5.0-p370. Raw tag counts were imported into R and Bioconductor, and differential gene expression was analyzed using the DESeq2 package. Transcription factor enrichment analysis was carried out using CHEA341.

Western-blotting. Protein extraction from liver tissues was carried out with RIPA Lysis Buffer containing phenylmethylsulfonyl fluoride, protease inhibitor, and phosphatase inhibitor. The protein concentration was determined using a bicinchoninic acid (BCA) assay kit (71285-M,Merck). Subsequently, the samples were subjected to electrophoresis on 8% SDS-polyacrylamide gels and transferred onto nitrocellulose membranes. The PVDF membranes were blocked with 5% skimmed milk for 2 hours, followed by an overnight incubation at 4°C with specific primary antibodies. The primary antibodies targeted the following proteins: Srebf1 (14088-1-AP, Proteintech), Lcat (12243-1-AP, Proteintech), Dgat2 (17100-1-AP, Proteintech), Cyp7a1 (ab65596, Abcam) and Beta-actin (ab8226, Abcam). The next day, the membranes were incubated with peroxidase conjugated goat anti-mouse IgG secondary antibodies (AP124P, Merck) or peroxidase conjugated donkey anti-rabbit IgG secondary antibodies (NA934, Cytiva). Protein bands were visualized using a Clarity Western ECL Substrate kit (170-5061, BIO-RAD).

ChIP-seq and RIME. Fresh liver tissues (chopped into small pieces) and HRO or Huh1 cells were crosslinked with 1% formaldehyde (28906, ThermoFisher) in PBS for 10 minutes for histone modifications. For TFs or coregulators, tissues/cells were double crosslinked with 2 mM disuccinimidyl glutarate (DSG) (20593, ThermoFisher) for 30 minutes, followed by 1% formaldehyde for 10 minutes. The reaction was stopped with glycine at a final concentration of 0.125 M for 5 minutes. Liver pieces were disaggregated in ice-cold PBS with protease inhibitor (12352204, Roche) using a dounce homogenizer, first with a loose pestle and then with a tight pestle (FB56691, Fisher Science). Nuclei were isolated using three lysis buffers: lysis buffer 1 (50 mM Hepes–KOH, pH 7.5, 140 mM NaCl, 1 mM EDTA, 10% glycerol, 0.5% IGEPAL CA-630, and 0.25% Triton X-100), lysis buffer 2 (10 mM Tris–HCl, pH 8.0, 200 mM NaCl, 1 mM EDTA, and 0.5 mM EGTA), and lysis buffer 3 (10 mM Tris–HCl, pH 8.0, 100 mM NaCl, 1 mM EDTA, 0.5 mM EGTA, 0.1% Na-deoxycholate, and 0.5% N-Lauroylsarcosine). The samples were then sonicated for 30 minutes (30 seconds ON/30 seconds OFF) using the Bioruptor Pico (Diagenode). Protein A Dynabeads (10001D, Invitrogen) were incubated overnight with the antibodies. Each lysate was immunoprecipitated with the following antibodies: control rabbit IgG (sc-2027, Santa Cruz, 1–5 μg), anti-H3K27ac (ab177178, Abcam, 1 μg), anti-H3K4me1 (ab8895, Abcam, 1 μg), anti-H3K4me2 (ab7766, Abcam, 1 μg), anti-H3K27me3 ((07-449, Millipore, 1 μg), and anti-CREBH (HPA040671, Sigma Aldrich, 1μg). The immunoprecipitated Protein A beads were washed 3 times with lysis buffer and sent for proteomics analysis (RIME39).

The samples for proteomics were subjected to SDS-PAGE. Coomassie-stained bands were manually excised from the gel. The in-gel digestion was performed. Samples were suspended in 0.1% TFA and analyzed using an Ultimate 3000 liquid chromatography system coupled to an Orbitrap QE HF (Thermo Fisher). Briefly, peptides were separated in a 30 min linear gradient starting from 3% B solution (0.1% formic acid, 89.9% acetonitrile) and increasing to 23% B over 25 min and to 38% B over 5 min, followed by washout with 95% B. The mass spectrometer was operated in data-dependent acquisition mode, automatically switching between MS and MS2. MS spectra (m/z 400–1600) were acquired in the Orbitrap at 60,000 (m/z 400) resolution and MS2 spectra were generated for up to 15 precursors with a normalized collision energy of 27 and an isolation width of 1.4 m/z. The MS/MS spectra were searched against the UniProt H. sapiens (UP000005640, downloaded on June 2020), and a customized contaminant database using Proteome Discoverer 2.5 with Sequest HT. The fragment ion mass tolerance was set to 0.02 Da and the parent ion mass tolerance to 5 ppm. Trypsin was specified as an enzyme. The following variable modifications were allowed: Oxidation (M), Deamidation (N, Q), Acetylation (N-terminus), Met-loss (M), and a combination of Met-loss and acetylation (N-terminus), whereas Carbamidomethylation (C) was set as a fixed modification. Peptide quantification was done using a precursor ion quantifier node with the Summed Intensity method set for protein abundance calculation.

For ChIP-seq library preparation, the same ChIP protocol was followed. Formaldehyde cross-linking was reversed overnight at 65°C. The ChIP DNA was purified using the ChIP DNA Clean and Concentrator Capped Zymo-Spin I (Zymo Research) purification kit. Two to four ChIPs were pooled during the final purification step to obtain concentrated material. For library preparation and sequencing, 2–10 ng of ChIPed DNA was processed using the Rubicon ThruPLEX DNA-seq kit (TAKARA) following standard protocols, and sequenced on the NovaSeq X Plus Series (150PE reads, Novogene).

ChIP-seq data analysis. Computations of mice data were performed using resources provided by Galaxy71. The computations of human data was enabled by resources in project sens2024555 provided by the National Academic Infrastructure for Supercomputing in Sweden (NAISS) at UPPMAX. Analysis was conducted as previously described66. Sequencing files (fastq) provided by Novogene (Cambridge, United Kingdom) or BEA core facility (Stockholm, Karolinska Institute), along with the raw data from published ChIP-seq datasets (KDM6A: GSE95890; HNF4A: GSE31477)40, were aligned to the NCBI38/mm10 version of the mouse reference genome or GRCh38/hg38 using Bowtie2. Peaks were identified using the HOMER package.

Peak heights were normalized to the total number of uniquely mapped reads and displayed in the Integrative Genomics Viewer (IGV) as the number of tags per 10 million tags. For statistical analysis of the peaks, raw tag counts were imported into R and Bioconductor, and the edgeR package was used to identify potential differential-binding sites.

CUT&Tag sample preparation. CUT&Tag sample preparation was performed following published protocols from the Henikoff lab72. Approximately 500,000 HRO or Huh1 cells were harvested and counted and were resuspended in Wash Buffer and mixed with Concanavalin A-coated magnetic beads. The mixture was incubated at room temperature to allow binding. Bead-bound cells were then washed and incubated with the anti-KDMT6A (PA5-31828, Invitrogen, 1 μg) primary antibodies specific to the target protein, followed by a secondary antibody (PA5-31828, EpiCypher) for bridging. Next, the bead-bound cells were incubated with pAG-Tn5 (79561, EpiCypher) in the presence of digitonin for tethering. After a brief incubation to allow the transposome to tether to chromatin sites targeted by antibodies, cells were washed and subjected to magnesium activation to induce tagmentation. This step facilitated DNA cleavage and simultaneous tagging with sequencing adapters. Following tagmentation, DNA was extracted and quantified using Qubit fluorometer (Thermo Fisher) following the instructions. Libraries were PCR-amplified with barcoded primers, cleaned up using SPRI beads, and subjected to quality control checks, including size selection and quantification. The prepared libraries were sequenced on the NovaSeq X Plus Series (150PE reads, Novogene, United Kingdom).

CUT&Tag data analysis. Sequencing data was processed using standard pipelines. Sequencing files (fastq) provided by Novogene (Cambridge, United Kingdom) were aligned to the GRCh38/hg38 version of the human reference genome using Bowtie2. Duplicate reads were removed, and peaks were called using the HOMER package to identify regions of significant enrichment. SEACR73 is used to call peaks and enriched regions from chromatin profiling data. ChIPseqSpikeInFree73 is used to normalize Data.

FXR agonist treatment in mice and human cell line. Twelve-week-old WT and LKO mice were administered either a vehicle (H2O) or GSK2324 (dissolved in H2O) via intraperitoneal injection at a dosage of 30 mg/kg body weight once daily for three days. The animals were allowed to feed ad libitum until the final day of treatment. On this day, GSK2324 (Sigma Aldrich) was administered starting at 9 am, followed by a four-hour fasting period. The mice were then sacrificed starting at 1 pm to maintain a consistent circadian cycle. For the cell experiments, total RNA was extracted from the human cell line HROhep03 following a 24-hour treatment with 1 µM GW4064. Subsequent experiments were then conducted using the isolated RNA.

LXR agonist treatment in mice hepatocytes and human cell line.
Twelve-week-old WT and LKO mice were sacrificed. GentleMACS was used to perfuse livers, dissociate the tissues, and isolate hepatocytes. Total RNA was extracted from the hepatocytes following a 6-hour treatment with 1 µM GW3965/T0901317 (Sigma Aldrich). For human cell line, total RNA was extracted from the hepatocytes following a 24-hour treatment with 1 µM T0901317.

Rare variant gene-level associations for KDM6A with serum ApoA1 and ApoB levels.
The data was derived from the Type 2 Diabetes Knowledge Portal website. Passing Variants indicate the number of variants in the analysis, which were grouped into sets using two criteria: variants predicted with high confidence by Loss-Of-Function Transcript Effect Estimator (LofTee), or missense variants as well as variants predicted with low confidence by Loss-Of-Function Transcript Effect Estimator (LofTee).

Statistical analysis. All the RNA-seq, ChIP-seq, CUT&Tag, qPCR experiments and the phenotype analysis in cell and mouse-based studies were conducted with biological replicates, with minimum n=3 in each group. The number of replicates and the statistical analysis in each experiment are described in the figure legends. Sample sizes were not predetermined statistically. The D’Agostino and Pearson normality test was employed to assess normal distribution. Variance within each data group was compared using the F-test for two groups or the Brown–Forsythe test for more than two groups. Statistical analyses were performed using GraphPad Prism 10 (GraphPad Software, Inc., La Jolla, CA), with data represented as mean ± standard error of the mean (s.e.m.). Statistical tests were conducted after confirming that the data met the appropriate assumptions of normality, homogenous variance, and independent sampling. All tests were two-tailed, with significance defined as p < 0.05. No statistical methods were used to predetermine sample size. Grubbs' test was performed to identify an outlier. In general, all samples and animals were included in the analysis.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_2": "The manuscript studies an underrepresented area – the molecular basis beyond the sexual dimorphism of the liver. The rational of the study is a known phenomenon that the expression of KDM6A (Lysine Demethylase 6A) differs between females and males and that its expression can be lost in liver cancers. KDM6A is located on X chromosome and since it can escape X inactivation it is expressed higher in female compared to the male tissues. By integrating transcriptomic and epigenomic techniques in human liver cell lines and liver-specific knockout mice and disease models, the authors aimed to investigate the physiological roles and regulatory mechanisms of the hepatic KDM6A. They promise to answer the question what is the metabolic function of KDM6A in regulating liver lipid metabolism, how such regulation is modulated in the chromatin epigenetic levels and whether liver KDM6A deficiency leads to sex difference in developing metabolic disorders. The questions are relevant and novel but I am not sure that the answers provided are always clear and sufficiently general. I question also whether the sex dimoprhism has been proven for the KDM6A-HNF4A-CREBH network.\nTo draw the conclusions the authors used two hepatic cell lines of different sexes (female line HROHep03 and male cell line Huh7, both from Cytion) HROHep03 is a primary liver adenocarcinoma cell line; Huh7 is a well-differentiated hepatocellular carcinoma (HCC) cell line. They also used mouse strains, Kdm6flox/flox developed by Cyagen and WT controls, with or without the mPCSK9 delivered by adenovirus.\nMajor remarks.\nStudies in cell lines: While both HROHep03 and Huh7 cell lines originate from liver cancers in individuals of different sexes, their cancerous nature and associated chromosomal abnormalities complicate the assessments of X-chromosome gene dosage. For precise determination, experimental analyses such as karyotyping, quantitative PCR, or RNA sequencing would be necessary to assess the X-chromosome content and gene expression levels in these cell lines. This would prove whether these two cell lines are good models to study KDM6A in relation to sex dimorphic differences in metabolic pathways, potentially leading to disorders. The authors should thus determine the X-chromosome and KDM6A gene dosage of both cell lines to illuminate some of their conclusions.\nFor example, the gene ontology analysis in KDM6A knockdown male and female liver cell lines showed similar cholesterol and lipoprotein modulating pathways, which is unexpected taking in consideration the envisioned higher expression of KDM6A in females.\nFor some later mechanistic studies it is not always clear why sometimes only HRO cell have been used while in other cases only Huh7. If any comparison is to be made one would expect parallel experiments in both cell lines, even if they are not optimal. Or at least a more structured representation of experiment and data flow, considering the sex dimorphism in the focus.\nStudies in the mouse models: Authors showed that the atherogenic lipoprotein profiles were only observed in female but not male KDM6A LKO mice. Was the background of the KDN6A flox/flox mice 100% B6? Since for B6 the escape from X-inactivation has been documented, but I am not sure for other mouse lines.\nChip-Seq – why was the Chip-seq analysis for methylation and acethylation KDM6A acitvities peformed only in the HRO cells? Is it true also for Huh7 cells (or male cells) that KDM6A regulation of lipoprotein metabolic genes is independent of H3K27me3 demethylation? I am not convinced that the Chip_seq data in the female floxed and LKO mouse liver data are confirmatory of the HRO cell data.\nTo identify the TFs responsible for KDM6A recruitment to the lipoprotein and cholesterol (LnC) genes - HNF4A knockdown reduced KDM6A peaks in both Huh7 and HRO cells, so based on the cell lines, this is a general and not sex-specific event.\nSuppl. Fig 4 (j) shows the heatmap that compares the downregulated genes in KDM6A LKO female mice and HNF4A LKO male mice (GSE173968). This is not specified in the text of the main paper: \"We also compared liver RNA-seq of the female LKO mice with HNF4A LKO mice (GSE173968)39, and found that around 30% of the downregulated genes in the KDM6A LKO mice were overlapping with HNF4A LKO\". Claiming that the data was consistent with the HRO results does not prove that this is a sex dimorphic event.\nKDM6A removal affects the binding and transcription activation of CREBH- the RNA-seq data from HRO and Huh7 cells identified several common TFs, where among the top candidates was also CREBH. Again, where is the sex dimorphism?\nThe title is over-ambitious since sex dimorphism was not experimentally proven for all components promised in the title.\n“Sex dimorphic KDM6A-HNF4A-CREBH network controls lipoprotein cholesterol metabolism and atherosclerosis via epigenetic reprograming of hepatocytes”\nThe statement “We have therefore identified a novel sex-specific cholesterol regulatory mechanism in the liver.” is over ambitious. The authors have to specify clearly what is indeed sexually dimorphic (and what common to both sexes), and based on which model the conclusions have been made. It is not a problem that the data from cell lines do not always correlate with the mouse data. The problem is “generalization” of findings in cases when the experiments do not support the case.\nMinor comments:\n- The background line of the Kdm6aflox/flox mice is not described.\n-There is no data availability statement and location where transcriptome/ChIP-seq is deposited.\n-Some smaller text corrections/typos on page 6 line 4,7, page 8 line 14, page 10 line 22 and page 12 line 14.\n-In the Method part CUT&Tag sample preparation authors state \"DNA was purified using SPRI beads and quantified using a fluorometric method\". Detail on \"fluorometric method\" should be added. It should also be stated what is the origin of “50.000 harvested cells”.", "reviewer_3": "In this study, the authors demonstrated that KDM6A regulates the expression of lipoprotein metabolism-related genes in a sex-specific manner. Using mice, they showed that hepatic loss of KDM6A results in elevated plasma cholesterol levels and accelerates the development of atherosclerosis in female mice but not in male mice. KDM6A is recruited to chromatin by HNF4A and, through CREBH, activates the transcription of lipid-related genes.\nThis is a very interesting study, clearly presented and well-written. However, I have some comments that could help improve this study.\nIn Figure 1, the authors showed that the loss of KDM6A leads to a decrease in mRNA levels encoding proteins involved in lipid and cholesterol metabolism, such as LIPC, HMGCR, and MTTP. This downregulation was conserved between human cells and mouse livers.\nInterestingly, despite the downregulation of many lipid-related genes, plasma lipid levels increased. In the absence of LDLR, there was a two-fold increase in VLDL. How is this possible? As shown in Supplemental Figure 1i, genes involved in VLDL biogenesis were decreased. Do the authors have an explanation for this discrepancy? The study mainly focuses on gene expression, but did the authors also observe effects at the protein level? Is the increase in VLDL due to enhanced VLDL biogenesis, increased cholesterol synthesis, or a defect in clearance? Essential pathways that has to be studied to explain the mechanism underlying the lipid phenotype, as the lipid phenotype cannot be fully explained by the gene expression data. Similarly, for liver lipid levels, there is no clear correlation between the decrease in lipid-related genes and liver lipid accumulation. Is lipogenesis increased, or beta-oxidation?\nNext, the authors showed that HNF4A is required for the recruitment of KDM6A to lipid gene loci. In Supplemental Figure 4j, they demonstrated an overlap in genes regulated by KDM6A and HNF4A in the mouse liver. What is the effect of HNF4A downregulation on plasma and liver lipids? Do these two models exhibit the same phenotype? Additionally, liver-specific CREBH-deficient mice share a similar phenotype with KDM6A LKO mice. The authors discuss the HDL phenotype but not the VLDL/LDL phenotype. Can they speculate on the underlying mechanism? The gene expression data do not seem to fully support the proposed explanation.\nMinor points:\nPage 8: \"..while at adjacent CLPTM1 loci (?):\" This appears to be an editing error that was not removed before submission.\nPage 10: \"KDM6A interplays [interacts?] with...\"—this may be an editing error.\nPage 12: \"KDM6A in regulating (??):\" This also seems to be a typo.", "reviewer_4": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts."}, "author_response": "Rebuttal letter. Chen et al. NCOMMS-25-11342A\nGeneral response to the reviewers’ comments\nWe thank all the reviewers for their very supportive comments and appreciate the intriguing issues raised and detailed suggestions made. We have carefully addressed each of these in our extensive experimental revision as outlined in the point-by-point response. We believe that with the inclusion of the revised experimental data we could further improve the quality, the clarity and the conceptual impact of our study.\nWe would like to highlight the following key improvements of issues raised by all reviewers:\n1) We have screened the transcriptomic datasets and identified a suitable male human liver cell line Huh1 for comparable analysis with HRO cells. We have also added karyotype results to our cell lines. We have repeated all the knockdown experiments in the Huh1 cells and found that KDM6A controlled very different transcriptomic signatures between the two cells, with lipoprotein and cholesterol metabolic pathways annotated only in HRO cells but not Huh1 cells, which was consistent with the female specific phenotypes in the LKO mice.\n2) We have also performed additional ChIPseq analysis in the Huh1 cells upon knockdown of KDM6A in Huh1 cells and discovered that the overall regulation of KDM6A in the Huh1 cells was also independent of its demethylase activities, despite the major difference in KDM6A gene targets between HRO and Huh1 cells.\n3)We have confirmed the coregulation of KDM6A and HNF4A in the Huh1 cells. We found that both transcriptomic and epigenetic changes caused by HNF4A loss were significantly correlated with KDM6A knockdown in Huh1 cells. However, genome widely KDM6A did not require HNF4A to bind to its target genes in Huh1 cells, in contrast to the drastic global KDM6A release upon HNF4A knockdown in the HRO cells.\n4) We have performed the transcription factor enrichment analysis in the Huh1 cells and found major difference from HRO cells. CREBH was only enriched in KDM6A and HNF4A coregulated genes in HRO cells but not Huh1 cells. We have in particular confirmed the consistent regulation of KDM6A on CREBH in the female mice by Crebh ChIPseq in both WT and LKO female liver samples. The results showed released binding of Crebh at multiple key cholesterol and lipoprotein metabolic genes such as Cyp7a1, Cyp8b1 and Lcat, etc. To our knowledge, this is the first Crebh ChIPseq in mouse liver samples. We believe this dataset will be valuable for further investigation of CREBH metabolic mechanisms in the liver.\n5) We have also performed extensive physiological analysis in the mice. We have applied FPLC to analyze the VLDL triglyceride and cholesterol production in the WT and LKO mice. We have also measured the bile acids in the plasma, liver and feces in WT and LKO mice. In combination with the newly added RNAseq results in the AAV-PCSK9 WT and LKO mice, we have proposed mechanisms to explain how disturbed bile acid synthesis, excretion and reuptake affected the cholesterol accumulation in the liver, leading to the enhanced VLDL cholesterol production. We have also identified reduced lipogenesis in the LKO mice liver, which potentially contributed to the slightly reduced lipoprotein and significantly lower liver triglyceride.\nOverall, we believe that our work now provides better evidence supporting the sex specificity of KDM6A/HNF4A/CREBH axis in controlling the epigenetic regulation linked with lipoprotein and cholesterol pathways and development of atherosclerosis.\n[...]"}, {"round": 2, "reviewer_comments": {"reviewer_2": "The authors addressed Reviewer 2’s concerns about sex-specific modeling by performing karyotype and X/Y chromosome analyses and identifying Huh1 as a suitable male human liver cell line for comparison with the female HRO cells. Huh1 cells were added because the previously used Huh7 line lacked the Y chromosome and thus could not accurately represent a male model; in contrast, Huh1 cells possess a normal male karyotype, including functional KDM6C/UTY expression. This is a crucial limitation when using cell lines to study sex differences. The authors performed also a new RNA-seq and ChIP-seq experiments in Huh1 cells, confirming that KDM6A regulation in males differs from females and is largely independent of its demethylase activity. The mechanistic evidence was expandee by analyzing HNF4A–KDM6A interactions and CREBH binding, demonstrating a sex-specific regulatory axis in lipid and cholesterol metabolism. The title has been modified to clarifiy the statements about sex dimorphism, methodological data and data-availability details have now been added.\nThere are still some remaining inconsistencies or concerns.\n1. Sex-dimorphism generalization remains overstated\nAlthough the authors revised the title and toned down some claims, parts of the Discussion still generalize sex-specific conclusions. Many experiments proving “sex specificity” rely on only one female (HRO) and one male (Huh1) liver cell line, with Huh-7 remaining in some cases. Since all are cancer-derived and have abnormal karyotypes, it is not certain that the observed effects truly represent physiological male–female differences. The study still partially equates “cell line sex” with biological sex dimorphism, which remains an overextension.\nI thus suggest “female” and “male” cell line to be replaced by “cell line derived from a female or from a male patient “\n2. Remaining limited validation of HNF4A–KDM6A–CREBH axis in vivo.\nThe mechanistic link between KDM6A, HNF4A, and CREBH is well supported in cell models, but less robust in mouse experiments. For instance, ChIP-seq and CUT&Tag evidence in mice support co-regulation of a few target genes (e.g., Apoa1), but direct biochemical evidence of complex formation in vivo is not shown. This weakens the conclusion that the full “KDM6A–HNF4A–CREBH regulatory axis” is sex-specific and functional in the intact liver.\n3. Statistical and replication transparency\nAlthough the Methods section now provides more statistical details, biological replicates and batch numbers for some omics experiments (especially ChIP-seq and CUT&Tag) remain unclear. The paper states that experiments were “repeated twice,” which may not be sufficient for reproducibility in multi-omics comparisons or shoudl be commented.\n4. To overcome some of the limitations that might prove to be difficult experimentally, I suggest to include a final paragraph on study limitations that would strengthen the Discussion. It should explicitly acknowledge:\n• the restricted number of human models used\n• potential confounding due to cancer-derived chromosomal instability,\n• the need for validation in primary hepatocytes or organoid systems,\n• and that the mechanistic interactions among KDM6A, HNF4A, and CREBH remain partly correlative.", "reviewer_3": "I would like to thank the authors for their efforts to improve the manuscript. Although they have included new and relevant data, I still have several points that need to be addressed.\nIn Figure 1J, the authors claim to show that the composition of VLDL is altered. However, Figure 1J presents plasma cholesterol levels during the VLDL secretion assay. Since the cholesterol levels at time point 0 are already much higher in the LKO mice compared to the WT, this conclusion cannot be made. It appears that the relative increase in plasma cholesterol during the poloxamer experiment is similar between the two groups.\nTo confirm that the VLDL composition is indeed altered, the authors should isolate the VLDL particles by centrifugation and determine the levels of apoB100, triglycerides (TG), total cholesterol (TC), and phospholipids in these particles. Only then can they conclusively state that the VLDL composition is changed.\nIn addition, what are the plasma ApoB100 levels in these mice? I expect an increase, which suggests more VLDL particles rather than a change in particle composition.\nI disagree that LCAT reduction explains the reduction in plasma TG levels. LCAT deficiency increases plasma TG levels but attenuates diet-induced ApoB-containing lipoproteins.\nWhat is the evidence that bile acid synthesis is reduced, not only in gene expression data? It suggests performing flux analysis of bile acids and determining bile acid concentration over time.", "reviewer_4": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts."}, "author_response": "Rebu%al le%er. Chen, at al. NCOMMS-25-11342B.\nGeneral response to the reviewers’ comments\nWe thank all the reviewers for their insightful comments and suggestions. We have carefully addressed the issues raised by the reviewers with more experiments or discussions, as outlined in the point-to-point responses. We believe that the quality and clarity of the revised manuscript has been improved.\n[...]"}, {"round": 3, "reviewer_comments": {"reviewer_2": "In the 2nd revision the authors have successfully addressed most of my remarks. It is important that they acknowledge also the limitations of the study, and by this avoid potential generalisation.\nThe notion that cholesterol networks differ between males and females is important since this results in marked sex differences in lipoprotein patterns and risk of developing atherosclerosis. The data confirm that KDM6A is a female-biased, X-linked epigenetic regulator essential for hepatic cholesterol and lipoprotein homeostasis, whose loss induces atherogenic lipid profiles specifically in females. The manuscript provides a mechanistic basis, consistent with female protection before menopause and increased vulnerability when protective regulation is lost (even if this is not specifically underlined in the manuscript) .\nI suggest the paper to be accepted in nature Communications.", "reviewer_3": "The authors answered my comments sufficiently. I have no further comments.", "reviewer_4": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts."}, "author_response": ""}]

Decision letter