Graphical abstract graphic file with name fx1.jpg [57]Open in a new tab Highlights * • A mouse model of long-term IH captures a human steatohepatitis transcriptional signature * • Long-term IH exposure boosts neutrophil and monocyte infiltration into the liver * • Multi-omics identify dysregulated markers implicated in sleep apnea and steatohepatitis * • In practice, our data advocate systematic sleep apnea tests for liver disease phenotyping __________________________________________________________________ Natural sciences; Biological sciences; Physiology; Animal physiology; Human Physiology Introduction Obstructive sleep apnea (OSA) is one of the most frequent chronic diseases, affecting nearly one billion people worldwide.[58]^1 The central component of OSA is the repetitive occurrence of upper airway collapse during sleep leading to the cyclical sequences of desaturation-reoxygenation known as intermittent hypoxia (IH). The severity of IH (i.e., “the hypoxic burden”) is the major contributor to the occurrence, aggregation, and progression of other common chronic diseases such as diabetes, hypertension, heart failure, non-alcoholic fatty liver disease (NAFLD), and cerebrovascular diseases.[59]^2^,[60]^3^,[61]^4 It has been established in animal models of NAFLD and from clinical studies that IH and OSA trigger and accelerate the transition from steatosis to non-alcoholic steatohepatitis (NASH) and fibrosis.[62]^4^,[63]^5^,[64]^6^,[65]^7 Inflammation plays a crucial role in the pathophysiology of NASH and OSA but to date the molecular mechanisms underlying this comorbid association are poorly characterized.[66]^8^,[67]^9^,[68]^10^,[69]^11 In humans and in mouse models, the specific role of IH is often masked by major confounders such as obesity, diabetes, and/or reduced physical activity. To circumvent these issues, we investigated both a model of lean mice exposed to long-term IH (16 weeks) and a cohort of lean OSA patients free of comorbidities (n = 71) using high-throughput hepatic transcriptomics, lipidomics, and targeted serum proteomics. Our hypothesis was that long-term IH is sufficient to induce the biological pathways found in human steatohepatitis transcriptomic datasets. In addition, we asked whether there is a unique set of biomarkers in mice and humans associated with hepatic and systemic inflammation. Confirmation of these hypotheses would support the notion that IH is an independent “hit” that can autonomously induce NASH in humans. Results A unique standard-diet mouse model exposed to long-term IH To explore the consequences of long-term exposure to IH during sleep on hepatic physiology, mice were housed under a 12 h light-dark cycle and fed with a regular chow diet. They were randomly assigned to either IH (1-min cycles of 5%–21% FiO[2]) or normoxic control (NC, 1-min cycles of 21% FiO[2]), 8 h per day during their sleep cycle (between ZT0 and ZT8, [70]Figure 1A) for up to 16 weeks. The body weight of mice subjected to IH decreased rapidly during the first 2 weeks and remained significantly and consistently lower throughout the experiment compared to the NC group ([71]Figure 1B). Food intake tends to decrease during the first week of exposure and then remains similar in between groups ([72]Figure S1A). It is interesting to note that fasted glycemia starts to increase significantly after 8 weeks of IH ([73]Figure 1C). A major strength of our experimental design is that mice exposure to 16 weeks of IH is quite unique in the field and reflects more accurately the long-term consequences of IH in human OSA. Figure 1. [74]Figure 1 [75]Open in a new tab Hepatic transcriptomic signatures of long-term intermittent hypoxia in mice (A) Experimental design of mouse model of sleep apnea. Lean male mice fed with regular chow diet were exposed to 16 weeks of intermittent hypoxia (IH) or normoxic cycles (NC). (B) Body weight of mice exposed to intermittent hypoxia (IH, n = 15) and normoxic control (NC, n = 15) measured once a week over the time course of the experiment. Dark bars and circles are for NC mice and gray bars and triangles for IH mice. Body weight was presented as mean + standard error of mean (SEM). Significance was calculated per each time point using a Student’s t test and ∗∗∗ indicates a p value ≤0.001. (C) Fasted glycemia of mice exposed to intermittent hypoxia (IH, n = 15) and normoxic control (NC, n = 15) measured once a week over the time course of the experiment. Fasted glycemia was presented as mean + standard error of mean (SEM). Significance was calculated per each time point using a Student’s t test and ∗∗∗ indicates a p value ≤0.001. (D) Pie chart representing the number of differentially expressed genes in the liver after 16 weeks of IH. Upregulated genes are shown in red and downregulated genes are shown in blue. Genes were selected using a p value ≤0.01 indicating a significant difference between IH and NC. (E) Gene ontology (GO) analysis showing the top six biological processes enriched in upregulated genes upon IH (top) denoted in red and downregulated genes upon IH (bottom) represented in blue. The number of dysregulated genes in our transcriptome over the total number of genes for each GO category is indicated on the graph. On the left panel, genes were selected using a p value ≤0.01 indicating a significant difference between NC and IH and ranked GO were ranked per p value. On the right panel, the top 500 genes with the highest expression difference between IH and NC were selected and GO were ranked per p value. Specific hallmarks of long-term IH on the hepatic transcriptome We first examined whether specific hepatic transcriptional reprogramming occurred after long-term exposure to IH. Hepatic RNA sequencing (RNA-seq) revealed that IH significantly dysregulates 1203 genes (p ≤ 0.01, [76]Figure 1D). To identify related biological functions, we performed pathway enrichment analysis using the Gene Ontology Biological Processes database, and upstream transcriptional regulator enrichment analysis using the ChIP enrichment (ChEA) database. Gene ontology analysis identified distinct enriched biological pathways differentially expressed in response to IH ([77]Figures 1E, and [78]S1B). Specifically, metabolic pathways involving lipids and nutrient utilization were significantly enriched in downregulated genes, while inflammatory processes were overrepresented in upregulated genes ([79]Figure 1E, top panel). Likewise, downregulated genes were mostly targets of PPARg, a transcription factor involved in fatty acid and glucose metabolism, whereas enriched upregulated genes were targets of GATA1 and STAT3, which are transcription factors implicated respectively in the maturation of blood cells and inflammation ([80]Figure S1). These observations were consolidated with different gene ontology resources to reinforce the robustness of the data ([81]Figure S1B). Collectively, these data demonstrate that IH is sufficient to induce molecular pathways found in steatohepatitis in humans mostly characterized by an increase in hepatic inflammation. Exposure to long-term IH alone in mice is enough to capture a human steatohepatitis molecular signature To evaluate the relevance of our IH mouse model to human pathology, we compared our RNA-seq with transcriptome that has been reported in humans with steatohepatitis. We performed biological pathway enrichment and upstream transcriptional regulator enrichment analysis on the top 500 most significantly dysregulated genes in human and mice transcriptomes and compared the 20 most significantly dysregulated pathways ([82]Figures 2A and [83]S2; [84]Table S2) and associated transcription factors ([85]Figure 2B and [86]S2; [87]Table S2). Downregulated genes present a common signature for fatty acid oxidation and PPARg target genes. Upregulated genes are enriched for neutrophils, inflammation, and GATA1 and NRF2 target genes ([88]Figures 2 and [89]S2; [90]Table S2). These results further support the notion that IH in mice is an independent “hit” sufficient to induce molecular markers implicated in human NASH. This occurred in the context of individuals under standard diet but subject to long-term IH exposure. Figure 2. [91]Figure 2 [92]Open in a new tab Comparison of hepatic signatures of human NASH and mouse long-term IH (A) Venn diagrams representing the comparison of the top twenty GO biological processes ranked by p value enriched in the top 500 upregulated genes (red circles, left panel) or the top 500 downregulated genes (blue circles, right panel) in IH versus NC mice (dark blue and dark red circles) and NASH versus healthy humans (light blue and light red circles). The top 500 genes were selected using a p value ≤0.01. (B) Venn diagrams representing the upstream transcriptional regulators ranked by p value associated with the top 500 upregulated genes (red circles, left panel) or the top 500 downregulated genes (blue circles, right panel) in IH versus NC mice (dark blue and dark red circles) and NASH versus healthy humans (light blue and light red circles). The top 500 genes were selected using a p value ≤0.01. Long-term IH exposure reprograms hepatic lipid metabolism The “multiple hit” hypothesis proposes that a continuum and potentially a combination of hepatic insults trigger and accelerate NAFLD development. Deregulation of lipid metabolism has been strongly associated with the onset and progression of NAFLD and our transcriptomic analyses confirmed that the PPARg transcription factor and the mitochondrial fatty acid oxidation pathway are central to liver injury pathogenesis in IH ([93]Figures 3A, 3B, [94]S2, and [95]S3). This is exemplified by the expression profiles of target genes such as Cd36, Pparg, and Hadh ([96]Figures 3C, [97]S3, and [98]S6). Therefore, this signature prompted us to analyze the hepatic lipid content through a mass spectrometry-based lipidomic approach ([99]Figure 3D). We found no consistent differences in terms of overall hepatic content of lipids but we discovered significative variations in the abundance of specific fatty acids species potentially involved in signaling/inflammation ([100]Figures 3D, 3E, and [101]S3). Among them, arachidonic acid (C20:4) is significantly increased in the liver during IH while eicosanoic acid (C20:0) is decreased ([102]Figure 3D and [103]S3). Arachidonic acid is the precursor for the synthesis of the pro-inflammatory molecules prostaglandins and leukotrienes ([104]Figure 3F). Interestingly, arachidonic acid is also increased in the serum of mice exposed to 16 weeks of IH ([105]Figure 3D and [106]S3). Hence, both lipidomic and transcriptomics analysis converge to indicate the occurrence of an inflammatory response to long-term IH. Figure 3. [107]Figure 3 [108]Open in a new tab IH reprograms fatty acid metabolism and PPARg-dependent gene expression (A) GSEA showing the enrichment of genes involved in mitochondrial fatty acid beta-oxidation and mitochondrial ATP synthesis coupled electron transport in the livers from the 16 weeks IH group. (B) Heatmap representing significant (p value ≤0.01) differentially expressed PPARg target genes in the livers from the 16 weeks IH group. (C) Representative expression of PPARg target and fatty acid metabolic genes determined by RT-qPCR at different length of IH exposure (4 and 16 weeks). Gene expression was normalized to beta-actin and presented as mean + standard error of mean (SEM, n = 4–5 biological replicates per group). Significance was calculated using a Student’s t test and ∗indicate a p value of 0.05. (D) Heatmap representing fatty acid profiles in the liver and serum of mice exposed to 16 weeks of IH determined by mass spectrometry. Each fatty acid abundance was calculated and normalized according to the internal standard and presented as mean + standard error of mean (SEM, n = 5 biological replicates per group). Significance was calculated using Student’s t test and ∗ indicates a p value ≤0.05. (E) Illustration depicting arachidonic acid metabolism and significant dysregulated fatty acid under IH and their link with inflammation. Long-term IH exposure promotes immune cell infiltration into the liver The distinctive inflammatory signature mediated by IH likely arises from the immune cell contribution in the bulk liver transcriptomic analysis. Remarkably, the top 500 most upregulated genes are typically found in leukocytes and are associated with inflammatory response ([109]Figure 1E, right panel). Furthermore, GSEA corroborates the overrepresentation of the leukocytic signature upon IH ([110]Figure 4A) and the comparison of mice and human transcriptomes pinpoints a predominant presence of neutrophils ([111]Figures 1E and [112]S2). These observations were further supported by immunohistochemistry experiments showing an increased number of neutrophils and monocytes in the liver of mice exposed to 16 weeks IH ([113]Figure 4B). It is important to note that no difference in leukocyte staining was found after 4 weeks of IH exposure ([114]Figure 4B) demonstrating again that long-term IH is required to boost hepatic immune cell infiltration and inflammation. Figure 4. [115]Figure 4 [116]Open in a new tab IH fosters immune cell infiltration and activation in the liver (A) GSEA showing the enrichment leukocytes, neutrophils, monocytes, and lymphocytes-specific genes in the livers from the 16 weeks IH group. (B) Representative immunohistochemistry of the neutrophil marker MPO, monocytes marker CD68, and lymphocytes marker CD3 in liver biopsies at 4 and 16 weeks of IH exposure and their respective controls. A comparative observation of cell content in the liver sections from different groups was quantified and expressed as a number of positive cell staining per section area. Two slides per animal (n = 4–5) were analyzed and are presented as mean + standard error of mean (SEM). Statistical significance was calculated per each time point using a Student’s t test,∗,∗∗ and ∗∗∗ indicates respective p value ≤0.05, 0.01, and 0.001. Long-term IH exposure in lean mice is associated with a hepatic and systemic inflammatory signature Consequently, we investigated whether long-term IH generates a peculiar inflammatory signature in the liver. GSEA confirmed the hepatic cytokine response and highlighted a specific set of inflammatory markers during IH ([117]Figures 5A and [118]S4). Among them, IH significantly upregulates the NLRP3 inflammasome and targets genes Il1a and Il1b, all involved in the development of chronic inflammation-related diseases ([119]Figure 5B). We then assessed the longitudinal time course of hepatic expression by RT-qPCR of representative inflammatory markers. Strikingly, the increased expression of Il1a, Il1b, and Il16 occurs only after long-term IH exposure ([120]Figure 5C). Figure 5. [121]Figure 5 [122]Open in a new tab IH induces a set of hepatic inflammatory biomarkers (A) GSEA showing the enrichment of genes involved in response to cytokine and inflammatory response in the livers from the 16 weeks IH group. (B) Heatmap illustrating genes characterizing the response to cytokine and the inflammatory response significantly upregulated in the 16 weeks IH group (p-value ≤0.01). (C) Representative inflammatory markers expression determined by RT-qPCR at different length of IH exposure (4 and 16 weeks). Gene expression was normalized to beta-actin and presented as mean + standard error of mean (SEM, n = 4–5 biological replicates per group). Significance was calculated using a Student’s t test and ∗, ∗∗ indicates respective p value of 0.05 and 0.01. The bidirectional interactions between local and systemic inflammation prompted us to search for a comprehensive immune response set of markers in serum from mice exposed either to short (4 weeks) or long-term IH (16 weeks). Cytokine profiling revealed a significant induction of circulating biomarkers IL16, INFB1, and TARC after 16 weeks of IH ([123]Figures 6A, 6B, and [124]S5). It is important to note that the circulating cytokines are not altered after a short exposure to IH, except the significant increase of IL1A. There is also a progressive evolution in hepatic inflammation and cytokine profile during exposure to NC or IH. Figure 6. [125]Figure 6 [126]Open in a new tab Cytokine profiling of IH mice and OSA patients (A) Cytokine analysis was performed using serum from NC and IH mice collected after 4 or 16 weeks exposure (n = 5 biological replicates per time point per group). Results are displayed as a heatmap and significance was calculated using Student’s t test and ∗ indicates a p value ≤0.05. (B) Comparison of cytokines profiles from serum from IH mice and OSA patients. Data are shown as mean + standard error of mean (SEM). Significance was calculated using Student’s t test and ∗ indicates a p value ≤0.05. Inflammatory signature in OSA patients We then investigated the relevance of this circulating cytokine signature in the serum of lean OSA patients. These patients came from a unique cohort of lean, middle-age OSA individuals free of any cardiovascular, hepatic, or metabolic comorbidities (cohort characteristics are shown in supplementary [127]Table S1). The goal here was to mirror the situation of lean mice exposed to long-term IH. Among the inflammatory markers found in mice, only IL16 and interferon B (IFNB) were significantly higher in OSA patients than in control subjects ([128]Figure 6B). Nevertheless, cytokine profiling of OSA patient samples also revealed a significant induction of IL20, LIF, TNF-α, and IFNg, consistent with trends found in mouse results ([129]Figure 6 and [130]S5). Discussion OSA and NAFLD are among the most frequent chronic diseases with a high burden for patients, society, and health systems.[131]^12^,[132]^13 Consistent data from clinical studies advocate that OSA contributes to the development and progression of NAFLD. However, these observational data are partly flawed by the difficulty in delineating the respective role of IH versus comorbid obesity, sedentarity, and/or diabetes. To identify specific IH-driven molecular alterations at the early stage of NAFLD development, we used a mouse model fed with a standard diet (i.e., lean mice) exposed for up to 16 weeks to IH to reflect long-term exposure to a hypoxic burden as it occurs in patients with OSA. The design of our study focused on how IH in isolation may impact hepatic molecular pathways rather than the parallel histological feature of NASH. This approach has allowed us to minimize secondary events such as steatosis and fibrosis and confounders that would be encountered using mouse models of NAFLD. The 16-week exposure to IH is a clear strenght as most of the studies available in the field do not extend beyond 4 weeks of exposure. This is of crucial importance as early acute adaptation of the animals to IH is known to have a significant impact on body weight and food intake ([133]Figures 1B and [134]S1) that might widely interfere with identification of mechanisms specific to IH. To depict the molecular portrait of the hepatic consequences of IH, we employed unbiased transcriptomic analysis and revealed that long-term IH without confounders is powerful enough to drive a gene expression cascade observed also in human NASH. This is an important addition to previous observational clinical studies showing that OSA severity (assessed through the apnea-hypopnoea index) is an independent predictor of liver disease progression.[135]^4 Comparison of IH mice and human NASH hepatic transcriptomes has revealed a common set of dysregulated genes, targets of PPARg and NRF2 transcription factors. These factors are well known to be implicated in liver disease and linked to metabolism and inflammation but they have not been described previously in rodent models of sleep apnea. It is important to note that current data are not supporting or providing a rationale for a pivotal role of hypoxia-inducible factors (HIF1a and HIF2) in the context of long-term IH ([136]Figure S6) suggesting distinct adaptive mechanisms to short- and long-term IH. In NASH patients, it has been demonstrated that NRF2 activation is correlated with liver inflammation.[137]^14 Interestingly, we have previously shown that short-term IH (2 weeks) is sufficient to alter NRFs-dependent hepatic gene expression without mediating any transcriptional changes in inflammatory genes. The current data constitute an addition to our knowledge in showing that sustained deregulation of NRFs target genes might contribute to the progression of liver disease. PPARs are key regulators of fatty acid and glucose metabolism, inflammation, and fibrosis and PPARs agonists have demonstrated promising improvement in liver disease in randomized controlled clinical trials.[138]^11^,[139]^15 Our data suggest that the association between NASH and OSA might be of particular interest for this pharmacological approach and this merits to be investigated in further studies dedicated to this comorbid association. Interestingly, PPARg lipolytic target genes are downregulated after 16 weeks of IH exposure in association with a remodeling of hepatic fatty acid content. Among them, arachidonic acid has been shown to be an early indicator of inflammation during NAFLD development.[140]^16^,[141]^17 Accordingly, our group has previously reported that the arachidonic acid-derived metabolite, leukotriene B4 (LTB(4)), is increased during OSA and is linked to early vascular remodeling.[142]^18^,[143]^19^,[144]^20 We demonstrated that long-term IH boosts leukocyte infiltration into the liver, which probably accounts for the strong inflammatory signature revealed by our bulk liver transcriptomic analyses. It would have been compelling to complement this analysis with single-cell and spatial transcriptomics and/or to characterize hepatic immune cells population by flow cytometry in order to precisely map the immune and inflammatory signature.[145]^21 Despite its limitations, this strategy allowed us to identify a set of inflammatory markers associated with long-term IH. Among them, IH significantly upregulates the NLRP3 inflammasome and the interleukins Il1a, Il1b, and Il16. Interestingly, the induction of IL16 has also been found in serum of mice exposed to long-term IH as well as in OSA patients. IL16 is a pro-inflammatory cytokine[146]^22^,[147]^23 that is chemotactic for CD4^+ T lymphocytes, monocytes, and eosinophils linked to various diseases such as asthma,[148]^24 rheumatoid arthritis,[149]^25 and inflammatory bowel disease.[150]^26^,[151]^27 Furthermore, recent studies have shown that IL16 expression in the liver correlates with inflammation in patients with primary biliary cholangitis and progression of liver hepatocellular carcinoma,[152]^28^,[153]^29 making IL16 an interesting therapeutic target. We also found a significant induction of IFNB in both IH mice and OSA patients. In addition, OSA patients present a significant increase of other interferons such as IFNg and IFNw. IFNs are essential cytokines of the immune system that serve as key effectors. Initially recognized for their crucial role in defending against viral infections, their involvement in diverse diseases has come to light in recent studies, revealing both protective and potentially harmful roles. Interestingly, several studies have shown that interferons affect the progression of non-alcoholic fatty liver disease.[154]^30 The cytokine profiling also revealed a significant induction of IL20, LIF, and TNF-α in OSA patients that parallels with the trends toward an increase in mice. It would be worthwhile to increase the number of biological replicates in mice to fully characterize the conserved signature between mice and humans. TNF-α has been described in various inflammatory diseases including NASH and OSA[155]^31^,[156]^32^,[157]^33 while little is known about IL20 and LIF in these pathologies.[158]^34 IL20 has been implicated in several inflammatory diseases[159]^35 and ongoing clinical trials are investigating the potential therapeutic effects of anti-IL20 in psoriasis and rheumatoid arthritis ([160]NCT01038674, [161]NCT01261767) making IL20 an interesting target to explore in the context of OSA and NASH. Overall, our unbiased multi-omics approach has revealed molecular signatures linking IH, NASH, and OSA. We identified group of dysregulated markers (PPARg, NRF2, arachidonic acid, IL16, IL20, IFNB, and TNF-α) that warrant further investigation in clinical trials as they might impinge on both hepatic and extrahepatic organs and participate toward the development of metabolic and cardiovascular complications associated with OSA[162]^8^,[163]^36^,[164]^37^,[165]^38 ([166]Figure 7). Figure 7. [167]Figure 7 [168]Open in a new tab Summary of findings Long-term IH in mice induces inflammatory pathways entangled in human sleep apnea and steatohepatitis. Both OSA and NAFLD are chronic diseases with different clusters of associated co-morbidities that impact individual prognosis.[169]^39 To implement precision medicine in both conditions, biomarkers reflecting this complexity of these pathologies are needed to guide the personalized management of the patients. The progression of liver disease is controlled by a variety of factors, including inflammatory cells and cytokines.[170]^9^,[171]^10 Our original data pave new avenues for investigating therapeutic targets related to the specific role of cytokines in the comorbid association between OSA and NAFLD. These observations also provide a strong rationale for studies conducted specifically on this co-morbid association. In clinical practice, our data support the systematic implementation of sleep apnea diagnostic tests in liver disease phenotyping. Limitations of the study Our study demonstrated that long-term IH significantly enhances leukocyte infiltration into the liver, likely contributing to the inflammatory signature identified through our bulk liver transcriptomic analyses. While our findings provide valuable insights, a more nuanced understanding could be achieved by complementing this analysis with advanced techniques such as single-cell and spatial transcriptomics. Additionally, characterizing the specific hepatic immune cell population through flow cytometry would offer a more precise mapping of the immune and inflammatory signatures, enhancing the depth of our investigation. In our exploration, we pinpointed a distinctive cluster of biomarkers, including pivotal transcription factors such as PPARg and NRF, as well as inflammatory cytokines like IL16 and TNF-α. However, our study refrains from definitively concluding the specific roles of each element. It is crucial to acknowledge that these mechanisms may interconnect, and our current investigation cannot firmly establish the individual contributions of PPARg, NRF, IL16, and TNF-α to IH-induced alterations in cellular metabolism and inflammation. Subsequent studies are imperative to unravel the intricate relationships among these factors and ascertain their respective impacts on the observed physiological changes. Our study uncovered a subtle, time-dependent signature, revealing distinct mechanisms of adaptation or maladaptation to short-term and long-term IH exposure, emphasizing the importance of characterizing the adaptive and maladaptive responses associated with varying durations of IH exposure. STAR★Methods Key resources table REAGENT or RESOURCE SOURCE IDENTIFIER Antibodies __________________________________________________________________ Rabbit Anti-CD68 Abcam ab12512 Rabbit Anti- MPO Abcam ab208670 Rabbit Anti-CD3 Abcam ab5690 Rabbit anti-HIF1a Abcam ab2185 Mouse anti-HIF1a Santa Cruz Biotechnology sc13515 Mouse anti- Tubuline-a Sigma Aldrich T5168 HRP Goat anti-Rabbit Jackson ImmunoResearch 111-035-003 HRP Goat anti-Mouse Jackson ImmunoResearch 115-035-003 Biotinylated Goat Anti-rabbit Vector Laboratories BA-1000 __________________________________________________________________ Biological samples __________________________________________________________________ Human Serum [172]ClinicalTrials.gov [173]NCT00764218 Mouse Serum This paper See [174]STAR Methods Mouse Liver This paper See [175]STAR Methods __________________________________________________________________ Chemicals, peptides, and recombinant proteins __________________________________________________________________ TRIzol reagent Invitrogen 15596026 DEPC treated water Invitrogen 4622224 Actinomycin D Sigma Aldrich A1410 Tridecanoic acid Sigma Aldrich 91988 Phosphatidylcholine Avanti 840009 Chloroform/methanol (1:2)-TMSH solution Macherey-Nagel 701520 Citrate buffer pH6 Sigma Aldrich C999 Hydrogen peroxide Sigma Aldrich H1009 Goat Serum Blocking Solution Vector Laboratories S-1000 HistoGreen Novus Biological E109 Mayer hematoxylin Sigma Aldrich 51275 Vectamount permanent medium Vectorlab H-5000 Bovine serum albumin Euromedex 04100812 Trichostatin A (TSA) Santa Cruz Biotechnology sc-3511 Nicotinamide Sigma Aldrich N0636 Dihydroethidium Sigma Aldrich 309800 Complete protease inhibitor Roche 04693132001 __________________________________________________________________ Critical commercial assays __________________________________________________________________ iScript complementary DNA (cDNA) synthesis kit BioRad Laboratories 1708840 SsoAdvanced SYBR Green Supermix kit BioRad Laboratories 1725270 SuperScript II reverse transcriptase Thermo Fischer Scientific 18064014 Vectastain Elite ABC kit peroxidase Vector Laboratories PK-6101 Avidin/biotin blocking kit Vector Laboratories SP-2001 4–20% Mini-PROTEAN TGX Precast Protein Gels BioRad Laboratories 4561096 __________________________________________________________________ Deposited data __________________________________________________________________ Mouse Liver Gene Expression Dataset GEO [176]GSE242668 Human Liver Gene Expression Dataset GEO [177]GSE33814 __________________________________________________________________ Experimental models: Organisms/strains __________________________________________________________________ Mouse: C57BL/6JRj Janvier Labs C57BL/6JRj __________________________________________________________________ Oligonucleotides __________________________________________________________________ Primers for RT-qPCR This paper See [178]STAR Methods __________________________________________________________________ Software and algorithms __________________________________________________________________ R software R project N/A Illumina software bcl2fastq v2.20.0.422 Illumina N/A SARTools GitHub.com N/A DESeq2 bioconductor.org N/A GSEA gsea-msigdb.org N/A MetaScape metascape.org N/A AmiGO geneontology.org N/A EnrichR maayanlab.cloud N/A DAVID david.ncifcrf.gov N/A Microsoft Office Microsoft N/A GraphPad Prism GraphPad N/A Affinity Designer Affinity N/A BioRender Biorender.com N/A Zen software Zeiss N/A __________________________________________________________________ Other __________________________________________________________________ LASQC diet Génobios Rod 16-R Precellys 24 tissue homogenizer Bertin Technologies N/A CK14 ceramic beads Bertin Technologies P000912-LYSK0-A Agilent Bioanalyzer Nano RNA Agilent N/A Nanodrop Thermofischer N/A NovaSeq 6000 Illumina N/A Gas chromatography-mass spectrometry Agilent 5977A-7890B Axio Scan microscope Zeiss N/A LSM510 microscope Zeiss N/A Luminex™ 200 system Eve Technologies Corp. N/A Roche Accu-Chek Guide glucometer Roche N/A [179]Open in a new tab Resource availability Lead contact Further information and requests for resources and reagents should be directed to and will be fulfilled by the lead contact, Jean-Louis Pépin (jpepin@chu-grenoble.fr). Materials availability The study did not generate new unique reagents. Data and code availability * • Data reported in this paper will be shared by the [180]lead contact upon request. RNA-seq data have been deposited at the Biotechnology Information Gene Expression Omnibus ([181]GEO) and are publicly available as of the date of publication. Hepatic mouse gene expression data reported in this paper is available under the accession number [182]GSE242668 . This dataset contains 10 samples, 5 from IH mice and 5 from NC mice. The hepatic human gene expression dataset used in this study was recovered from the GEO repository under the accession number [183]GSE33814. It consisted of 25 samples, 12 of which were steatohepatitis and 13 were controls. * • This paper does not report original code. * • Any additional information required to reanalyze the data reported in this paper is available from the [184]lead contact upon request. Experimental model and study details Animal handling and ethics approval Sixteen-week-old male C57BL/6JRj mice (Janvier Labs, France) were housed with ad libitum food and water access on a 12 h light/12 h dark cycle (light ON at 8 am = ZT0 and light OFF at 8 pm = ZT12). Mice were maintained with regular chow diet (LASQC diet, Rod 16, Altromin international) during all the experiment. Mice were randomly assigned to either intermittent hypoxia (IH) or normoxic control (NC) and directly exposed in their housing cages to 16 weeks of NC or IH, 8h per day during their sleeping period (IH from 8 am = ZT0 to 4 pm = ZT8 and NC the rest of the day) ([185]Figure 1A). The IH stimulus consisted of 60 sec cycles alternating 30 sec of hypoxia (hypoxic plateau at 5% FiO[2]) and 30 sec of NC (normoxic plateau at 21% FiO[2]). NC mice were exposed to similar air-air cycles in order to avoid bias from noise and turbulence related to gas flow. After 16 weeks of exposure, mice were sacrificed by decapitation, serums were collected and livers were harvested and immediately frozen in liquid nitrogen until further analysis. All experimental procedures were carried out in accordance with European Directive 2010/63/UE. They were reviewed by the Institutional Ethics Committee for Animal Care and Use (Cometh 12) and authorized by the French Ministry of Research (APAFIS# 15156-2018051615245109). Human biological samples: study population and ethics approval Biological samples from a case-controlled study (InfraSAS) of patients without any known cardiovascular comorbidity comprised 71 patients: 19 with severe OSA (AHI>30), 27 with moderate OSA (15