Membrane-bound O-acyltransferase 7 (MBOAT7) shapes lysosomal lipid homeostasis and function to control alcohol-associated liver injury

  1. Venkateshwari Varadharajan
  2. Iyappan Ramachandiran
  3. William J Massey
  4. Raghav Jain
  5. Rakhee Banerjee
  6. Anthony J Horak
  7. Megan R McMullen
  8. Emily Huang
  9. Annette Bellar
  10. Shuhui W Lorkowski
  11. Kailash Gulshan
  12. Robert N Helsley
  13. Isabella James
  14. Vai Pathak
  15. Jaividhya Dasarathy
  16. Nicole Welch
  17. Srinivasan Dasarathy
  18. David Streem
  19. Ofer Reizes
  20. Daniela S Allende
  21. Jonathan D Smith
  22. Judith Simcox
  23. Laura E Nagy
  24. J Mark Brown  Is a corresponding author
  1. Department of Cancer Biology, Lerner Research Institute of the Cleveland Clinic, United States
  2. Center for Microbiome and Human Health, Lerner Research Institute, Cleveland Clinic, United States
  3. Northern Ohio Alcohol Center (NOAC), Lerner Research Institute, Cleveland Clinic, United States
  4. Department of Biochemistry, University of Wisconsin-Madison, United States
  5. Department of Inflammation and Immunity, Lerner Research Institute, Cleveland Clinic, United States
  6. Department of Cardiovascular and Metabolic Sciences, Lerner Research Institute of the Cleveland Clinic, United States
  7. Center for Gene Regulation in Health and Disease (GRHD), Cleveland State University, United States
  8. Department of Pharmacology & Nutritional Sciences, Saha Cardiovascular Research Center, University of Kentucky College of Medicine, United States
  9. Department of Family Medicine, Metro Health Medical Center, Case Western Reserve University, United States
  10. Lutheran Hospital, Cleveland Clinic, United States
  11. Department of Anatomical Pathology, Cleveland Clinic, United States

Abstract

Recent genome-wide association studies (GWAS) have identified a link between single-nucleotide polymorphisms (SNPs) near the MBOAT7 gene and advanced liver diseases. Specifically, the common MBOAT7 variant (rs641738) associated with reduced MBOAT7 expression is implicated in non-alcoholic fatty liver disease (NAFLD), alcohol-associated liver disease (ALD), and liver fibrosis. However, the precise mechanism underlying MBOAT7-driven liver disease progression remains elusive. Previously, we identified MBOAT7-driven acylation of lysophosphatidylinositol lipids as key mechanism suppressing the progression of NAFLD (Gwag et al., 2019). Here, we show that MBOAT7 loss of function promotes ALD via reorganization of lysosomal lipid homeostasis. Circulating levels of MBOAT7 metabolic products are significantly reduced in heavy drinkers compared to healthy controls. Hepatocyte- (Mboat7-HSKO), but not myeloid-specific (Mboat7-MSKO), deletion of Mboat7 exacerbates ethanol-induced liver injury. Lipidomic profiling reveals a reorganization of the hepatic lipidome in Mboat7-HSKO mice, characterized by increased endosomal/lysosomal lipids. Ethanol-exposed Mboat7-HSKO mice exhibit dysregulated autophagic flux and lysosomal biogenesis, associated with impaired transcription factor EB-mediated lysosomal biogenesis and autophagosome accumulation. This study provides mechanistic insights into how MBOAT7 influences ALD progression through dysregulation of lysosomal biogenesis and autophagic flux, highlighting hepatocyte-specific MBOAT7 loss as a key driver of ethanol-induced liver injury.

eLife assessment

Varadharajan et al. explore the mechanistic basis of MBOAT7 SNP association with steatotic liver disease and link its function in LPI acylation to altered lipidomics of endosomal/lysosomal system and impaired TFEB mediated lysosomal biogenesis. The findings are important with theoretical and practical implications in MAFLD, alcohol-induced hepatic steatosis, and lysosomal diseases. The strength of evidence is convincing using methodology in line with current state-of-the-art.

https://doi.org/10.7554/eLife.92243.3.sa0

Introduction

End-stage liver diseases account for approximately 2 million deaths annually worldwide, with nearly half of liver disease-associated deaths arising from complications of alcohol-associated and non-alcoholic fatty liver disease (NAFLD)-related cirrhosis, and the other half driven by viral hepatitis and hepatocellular carcinoma. It is generally appreciated that there are some shared mechanisms driving liver injury from viral, NAFLD, or alcohol-associated liver disease (ALD)-driven etiologies, but also etiology-specific drivers that uniquely shape the pathogenesis of liver failure. Although there has been great progress in identifying the ‘multiple hits’ that lead to end-stage liver disease, we are only beginning to understand the cellular and molecular mechanisms driving etiology-specific liver disease progression. Within the evolving ‘multiple hit’ theory of liver disease progression, it is clear that interactions between environmental factors (i.e., diet, microbiome, alcohol, viral infection, environmental toxins, etc.) and genetic determinants uniquely contribute to liver injury (Cohen et al., 2011; Rinella and Sanyal, 2016). Currently, the only option for end-stage liver disease is liver transplantation. However, the availability of viable donor livers is finite, and pharmacological approaches to improve outcomes are simply lacking due to our poor understanding of the underlying mechanisms of disease pathogenesis. Given this, there is a clear need to understand the genetic and environmental interactions promoting the progression of liver disease from simple steatosis to more advanced inflammatory and fibrotic disease. We address this gap here by investigating the mechanisms linking a recently identified liver disease susceptibility gene in combination with alcohol exposure.

Genome-wide association studies (GWAS) provide a powerful unbiased tool to identify new genes contributing to human disease, allowing for pinpoint accuracy in identification of new potential drug targets. This is exemplified by the recent success story of GWAS discoveries leading to rapid development of monoclonal antibodies targeting proprotein convertase subtilisin/kexin type 9 (PCSK9) for hyperlipidemia and cardiovascular diseases (Sabatine, 2019). Since 2015, several independent GWAS studies have identified a liver disease susceptibility locus (rs641738) near the genes encoding MBOAT7 and TMC4 (Buch et al., 2015; Mancina et al., 2016; Krawczyk et al., 2017; Thabet et al., 2016; Thabet et al., 2017; Teo et al., 2021). It is important to note that the rs641738 T-allele (~43% allele frequency in European ancestry populations) is associated with all major forms of liver injury including NAFLD, ALD, and viral hepatitis-induced fibrosis (Buch et al., 2015; Mancina et al., 2016; Krawczyk et al., 2017; Thabet et al., 2016; Thabet et al., 2017; Teo et al., 2021). The rs641738 variant is associated with a C>T missense single-nucleotide polymorphism (SNP) within the first exon the TMC4 gene, but the GTEx project shows that TMC4 is not abundantly expressed in human liver (1.4 transcripts per million). We previously showed that mice lacking Tmc4 (Tmc4−/−) have normal high fat diet-induced hepatic steatosis (Gwag et al., 2019). We also recently demonstrated that antisense oligonucleotide (ASO)-mediated knockdown of Mboat7 promotes insulin resistance, hepatic steatosis, hepatocyte death, inflammation, and early fibrosis in high fat diet-fed mice (Gwag et al., 2019). In parallel, four independent groups also showed that Mboat7 loss of function promotes hepatic steatosis, inflammation, and fibrosis in mice (Meroni et al., 2020; Tanaka et al., 2021; Thangapandi et al., 2021; Xia et al., 2021). Collectively, MBOAT7 is a genetic determinant of advanced liver disease, but how this gene shapes susceptibility to environmental cues is still an area of intense investigation.

The MBOAT7 gene encodes a lysophospholipid acyltransferase enzyme (also known as lysophosphatidylinositol acyltransferase 1, LPIAT1), which uniquely contributes to the Land’s cycle of membrane phospholipid remodeling (Shindou and Shimizu, 2009). The Land’s cycle is a series of phospholipase-driven deacylation and lysophospholipid acyltransferase-driven acylation reactions that shape membrane asymmetry and diversity (Shindou and Shimizu, 2009). It is important to note that MBOAT7 selectively diversifies the fatty acid composition of membrane phosphatidylinositol (PI) species and not phospholipids with other head groups and exhibits acyl chain specificity for polyunsaturated fatty acids (Shindou and Shimizu, 2009; Gijón et al., 2008; Zarini et al., 2014). This substrate specificity has been observed in in vitro or cell-based studies (Gijón et al., 2008; Zarini et al., 2014), which has been confirmed in vivo in mice with diminished Mboat7 function (Gwag et al., 2019; Meroni et al., 2020; Tanaka et al., 2021; Thangapandi et al., 2021; Xia et al., 2021). Although MBOAT7 is well documented to directly modulate PI lipids, the Land’s cycle is highly dynamic and has the potential to influence many downstream metabolic processes as well as cell signaling. Here, we report that ethanol-induced perturbation of the hepatic lipidome is powerfully shaped by MBOAT7 function in hepatocytes. This MBOAT7-dependent reorganization of the hepatic lipidome in response to ethanol is also functionally tied to diminished lysosome function and defective autophagy. This work shows that MBOAT7 uniquely contributes to ethanol-induced liver injury via perturbations of hepatic lipid metabolism that extend beyond the direct remodeling of membrane PI.

Results

Heavy drinkers have reduced circulating levels of MBOAT7 enzymatic products

Given previous studies have shown that MBOAT7 is a risk locus for alcohol-associated cirrhosis (Buch et al., 2015), we investigated whether active alcohol consumption was associated with alterations in MBOAT7 function. To address this, we measured both lysophosphatidylinositol (LPI) substrates and PI products of the MBOAT7 enzymatic reaction in the circulation of healthy controls compared to confirmed heavy drinkers (Figure 1). Heavy drinkers were recruited and defined by an AUDIT score (Fleming et al., 1991) greater than 16, and compared to an age- and sex-matched healthy control population (Figure 1—source data 1). In agreement with genetic studies linking MBOAT7 variants to alcohol-associated cirrhosis (Buch et al., 2015), we find that circulating levels of metabolic products of MBOAT7 including arachidonic acid- and eicosapentaenoic acid-containing phophosphatidylinositols (PI 38:4 and PI 38:5) are significantly reduced in heavy drinkers compared to age-matched healthy controls (Figure 1). Given MBOAT7 demonstrates specificity for polyunsaturated (PUFA) acyl-CoA substrates (Gijón et al., 2008), it is important to note that only select PUFA-containing MBOAT7 products (PI 38:4 and PI 38:5) were reduced in heavy drinkers, whereas all other molecular species of PI were unaltered. We also examined the circulating levels LPI substrates of MBOAT7 but found no significant differences between controls and heavy drinkers (Figure 1). These data show that excessive alcoholic intake is associated with reduced levels of MBOAT7 product lipids, which further bolsters the concept that MBOAT7 loss of function may be causally linked to ALD progression.

MBOAT7 products are selectively reduced in heavy drinkers.

Plasma lysophosphatidylinositol (LPI – inset graph) and phosphatidylinositol (PI) species from both male and female healthy controls and heavy drinkers were measured by liquid chromatography–tandem mass spectrometry (LC–MS/MS). n = 10–16; ***p < 0.001 and ****p < 0.0001 in the figure. Analysis of variance (ANOVA) with Tukey’s post hoc test.

Figure 1—source data 1

Demographic and clinical parameters for the entire cohort of healthy controls and heavy drinkers recruited for this study.

https://cdn.elifesciences.org/articles/92243/elife-92243-fig1-data1-v1.docx

MBOAT7 loss of function in hepatocytes, but not myeloid cells, facilitates ethanol-induced liver injury in mice

Although there is some emerging evidence that MBOAT7 genetic variants may predispose humans to alcohol-induced liver injury (Buch et al., 2015), not all human studies have found a significant association (Zhang et al., 2018; Stickel et al., 2018; Beaudoin et al., 2021). Importantly, a causal relationship between MBOAT7 and alcohol-induced liver injury has never been established to date. To address this, we have studied ethanol-induced liver injury in mice selectively lacking Mboat7 in hepatocytes or myeloid cells, given the key roles that hepatocytes and myeloid cells play in the pathogenesis of ethanol-induced liver disease progression. To generate congenic hepatocyte-specific (Mboat7-HSKO) and myeloid-specific (Mboat7-MSKO) Mboat7 knockout mice we crossed mice harboring a post-FLP recombinase conditionally targeted Mboat7 floxed allele (Anderson et al., 2013; Massey et al., 2023) to mice transgenically expressing Cre recombinase under the albumin promoter/enhancer (Postic et al., 1999) or Cre knocked into the M lysozyme locus (Clausen et al., 1999), respectively. These independent Mboat7-HSKO and Mboat7-MSKO lines were then backcrossed mice >10 generations into the C57BL/6J background and subsequently subjected to ethanol exposure. Compared to control mice (Mboat7flox/flox), Mboat7-HSKO mice had significantly reduced Mboat7 mRNA and protein expression in the liver (Figure 2A, B), but not in other tissues (data not shown; Massey et al., 2023). Hepatocyte-specific deletion of Mboat7 resulted in enhanced ethanol-induced increases in liver weight and high concentrations of plasma alanine aminotransferase (ALT) (Figure 2C, D). Likewise, Mboat7-HSKO mice showed elevated hepatic steatosis scores and triglyceride levels under both pair-fed and ethanol-fed conditions (Figure 2E, F). However, hepatocyte-specific deletion of Mboat7 did not significantly alter the mRNA expression for several proinflammatory cytokines/chemokines including tumor necrosis factor α (Tnfa), transforming growth factor β (Tgfb), monocyte chemoattractant protein 1 (Mcp1), or interleukins 1β (Il1b) or 6 (Il6) in either pair- or ethanol-fed conditions (Figure 2G). These data demonstrate that MBOAT7 function in hepatocytes is a critical determinant of ethanol-induced liver injury, but we also wanted to explore a potential role for MBOAT7 in non-parenchymal cells given the key roles that macrophages and neutrophils play in ALD.

Figure 2 with 1 supplement see all
Hepatocyte-specific deletion of Mboat7 promotes ethanol-induced liver injury.

Female control (Mboat7fl/fl) or hepatocyte-specific Mboat7 knockout mice (Mboat7-HSKO) were fed with subjected the NIAAA (National Institute on Alcohol Abuse and Alcoholism) model of ethanol-induced liver injury. (A) Hepatic Mboat7 expression was measured via quantitative polymerase chain reaction (qPCR). (B) Western blot for hepatic microsomal MBOAT7 protein levels replicated in n = 3 mice. (C) Liver weight, (D) plasma alanine aminotransferase (ALT), (E) percent steatosis quantified by a blinded board-certified pathologist, (F) hepatic triglycerides, and (G) hepatic expression of inflammatory gene measured by qPCR. n = 5–7. Data represent the mean ± standard error of the mean (SEM) and groups not sharing a common letter superscript differ significantly (p ≤ 0.05).

Figure 2—source data 1

Original file for the western blot analysis in Figure 2B (anti-MBOAT7 and anti-β-actin).

https://cdn.elifesciences.org/articles/92243/elife-92243-fig2-data1-v1.zip
Figure 2—source data 2

PDF containing Figure 2B and original scans of the relevant western blot analysis (anti-MBOAT7 and anti-β-actin) with highlighted bands and sample labels.

https://cdn.elifesciences.org/articles/92243/elife-92243-fig2-data2-v1.zip

It is important to note during the preparation of this manuscript, an independent study discovered a potential role for MBOAT7 in suppressing toll-like receptor signaling and proinflammatory cytokine production in macrophages and Kupffer cells in the context of NAFLD (Alharthi et al., 2022). Furthermore, early studies examining the expression and substrate specificity for diverse lysophospholipid acyltransferases showed that MBOAT7-driven PI remodeling was highly active in human neutrophils where it can modulate the production of proinflammatory arachidonic acid-derived lipid mediators (Gijón et al., 2008). Therefore, we generated myeloid-specific (Mboat7-MSKO) Mboat7 knockout mice to further interrogate cell autonomous roles in ALD progression. First to confirm efficient deletion in myeloid cells, we isolated both bone marrow-derived and thioglycolate-elicited macrophages from control and myeloid-specific (Mboat7-MSKO) Mboat7 knockout mice, which confirmed essentially no detectable MBOAT7 protein in Mboat7-MSKO mice (Figure 2—figure supplement 1). In contrast to the enhanced ethanol-induced liver injury seen in Mboat7-HSKO mice (Figure 2), myeloid-specific (Mboat7-MSKO) Mboat7 deletion resulted in unaltered ethanol-induced effects on body weight, liver weight, circulating levels of aspartate and alanine aminotransferases (AST and ALT), liver triglyceride, and cytokine expression (Figure 2—figure supplement 1). Collectively, these results demonstrate that MBOAT7 loss of function in hepatocytes, but not myeloid cells, facilitates ethanol-induced liver injury in mice.

Ethanol exposure reorganizes the hepatic lipidome in a MBOAT7-dependent manner

Given the fact that ethanol exposure is well known to reorganize hepatic lipid metabolism, we performed comprehensive lipidomic profiling of the liver to understand how MBOAT7 could potentially shape ethanol-induced lipid metabolism in the liver. First, we used a targeted approach to measure the levels of MBOAT7 substrate LPIs and product PIs. Compared to control mice (Mboat7flox/flox), Mboat7-HSKO mice had significant accumulation of palmitate- and oleate-containing LPI substrate lipids (LPI 16:0 and LPI 18:1), with large accumulation of LPI 16:0 under ethanol-fed conditions (Figure 3A). When we examined PI species, we confirmed previous findings that Mboat7-HSKO mice have reduced levels of the major arachidonic acid-containing PI (PI 38:4) and also striking reductions in PI 38:3 under both pair- and ethanol-fed conditions (Figure 3B). In addition, Mboat7-HSKO mice also have accumulation of several other PI species including PI 34:1, PI 36:1, PI 36:2, PI 38:6, and PI 40:6, some of which are exacerbated in the ethanol-fed group (Figure 3C). Although many of these changes in MBOAT7’s substrate LPIs and product PIs are expected based on MBOAT7’s substrate specificity and previous literature, there is a clear interaction between ethanol and MBOAT7 uncovered here that has not been observed in studies using NAFLD-related models (Tanaka et al., 2021; Thangapandi et al., 2021; Xia et al., 2021).

Figure 3 with 11 supplements see all
Ethanol alters the liver lipidome in a MBOAT7-dependent manner.

Mboat7fl/fl or Mboat7-HSKO mice were subjected to the NIAAA model of ethanol exposure. Liver lysophosphatidylinositol (LPI) (A) and phosphatidylinositol (PI) species, including the MBOAT7 product PI 38:4 (B) and others (C), were quantified via liquid chromatography–tandem mass spectrometry (LC–MS/MS) in n = 5–7. (D) Principal component analysis for untargeted lipidomics analysis. The first and second principal components are plotted on the x- and y-axis, respectively, and sample treatment group is indicated by color. (E) Heatmap showing global lipidomic alterations in mouse liver. Total levels of endosomal/lysosomal lipids were measured by targeted and untargeted lipidomic approach using LC–MS/MS. (F) Total bis(monoacylglycero)phosphate (BMP) levels. (G) Total phosphatidylglycerol (PG) and (H) total cardiolipin (CL) from the liver of Mboat7fl/fl or Mboat7-HSKO mice. Data represent the mean ± standard error of the mean (SEM) and groups not sharing a common letter superscript differ significantly (p ≤ 0.05).

Given this unexpected interaction between ethanol exposure and MBOAT7 within the inositol-containing phospholipid pool, we examined the hepatic lipidome more broadly. Using untargeted lipidomics, we identified a striking remodeling of the global hepatic lipidome upon ethanol feeding in Mboat7-HSKO mice (Figure 3; Figure 3—figure supplements 111). Unexpectedly, we observed a large increase in the levels of endosomal/lysosomal lipids including bis(monoacylglycero)phosphates (BMPs) and their outer mitochondrial membrane precursor phosphatidylglycerols (PGs) in ethanol-exposed Mboat7-HSKO mice (Figure 3E–G; Figure 3—figure supplements 3 and 4). In addition, Mboat7-HSKO mice had elevated levels of cardiolipin species, which are known to localize to mitochondria (Figure 3E and H; Figure 3—figure supplement 5). Under the pair- and ethanol-fed conditions studies here, Mboat7-HSKO mice also exhibited some more minor alterations in certain species of phosphatidylcholine (PC), phosphatidylethanolamine (PE), phosphatidylserine (PS), phosphatidic acid (PA), sphingomyelin (SM), ceramides (Cer), and ether-linked phospholipids (Figure 3E; Figure 3, Figure 3—figure supplements 2; 711). Although several recent studies examining hepatocyte-specific Mboat7-HSKO mice have found more limited effects on the global lipidome under experimental conditions designed to stimulate non-alcoholic steatohepatitis (Tanaka et al., 2021; Thangapandi et al., 2021; Xia et al., 2021), here we show that upon ethanol exposure, hepatocyte MBOAT7 plays a major role in shaping endosomal/lysosomal lipid homeostasis.

Mboat7-HSKO mice have dysregulated lysosomal function in response to ethanol

Given the accumulation of endosomal/lysosomal lipids including BMPs seen in ethanol-fed Mboat7-HSKO mice, we hypothesized that ethanol may perturb lysosome function in a MBOAT7-driven manner to promote liver injury. It is well known that BMPs commonly accumulate in both drug-induced and genetic lysosomal storage disorders (Gruenberg, 2020; Showalter et al., 2020; Hullin-Matsuda et al., 2014), and due to their cone-shaped structure BMPs can contribute to significant membrane asymmetry that impacts intracellular lipid sorting, apoptosis, and autophagic flux (Gruenberg, 2020; Showalter et al., 2020; Hullin-Matsuda et al., 2014). At the same time, there is emerging evidence that chronic ethanol exposure can reduce the expression of transcription factor EB (TFEB), which is a master regulator of lysosomal biogenesis and autophagy-associated gene expression (Chao et al., 2018). Given the role that lysosomal dysfunction plays in ethanol-induced liver injury (Chao et al., 2018; Bala and Szabo, 2018), and the unexpected accumulation of BMP lipids in Mboat7-HSKO mice, we next investigated TFEB-mediated lysosomal biogenesis and autophagy regulation in Mboat7-HSKO mice challenged with ethanol (Figure 4). First, Mboat7-HSKO mice showed elevated levels of key autophagy regulatory proteins LC3-I/II and p62 in the liver, particularly under ethanol-fed conditions (Figure 4A). Interestingly, Mboat7-HSKO mice also had increased total levels of the mammalian target of rapamycin (mTOR) (Figure 4A), which is a well-known master regulator of autophagic flux. The accumulation of p62 and LC3-I/II cannot distinguish between enhanced or defective autophagic flux, so we next examined potential alterations in lysosome abundance and function. Interestingly, both mRNA and protein levels of lysosomal marker proteins LAMP-1 and LAMP-2 were reduced in ethanol-fed Mboat7-HSKO mice (Figure 4A, D). Furthermore, compared to control mice fed ethanol, we found that ethanol Mboat7-HSKO mice had generally reduced expressions levels of TFEB target genes and proteins associated with lysosome acidification and lipid turnover including ATPase H+ transporting V1 subunits A, H, and D (Atp6v1a, Atp6v1h, and Atp6v1d), α galactosidase A (Gla), chloride channel 7 α (Clcn7), and mucolipin TRP cation channel 1 (Mcoln1) (Figure 4A, D). Similarly, the mRNA expression, and total and nuclear protein abundance of TFEB was reduced in ethanol-fed Mboat7-HSKO mice compared to ethanol-fed Mboat7flox/flox control mice (Figure 4A, B and D). In contrast, the expression of key autophagy related genes including Atg2b, Atg3, Atg7, Atg8, and Unc-51-like autophagy activating kinase 1 (Ulk1) were significantly elevated in ethanol-fed Mboat7-HSKO mice compared to ethanol-fed Mboat7flox/flox control mice (Figure 4D).

Figure 4 with 2 supplements see all
Mboat7-HSKO mice have dysregulated lysosome function in response to ethanol.

Age-matched female Mboat7fl/fl or Mboat7-HSKO mice were subjected to the NIAAA model of ethanol exposure. (A) Total liver lysates were subjected to western blot analysis of major autophagy marker genes LC3A/B (P62), mammalian target of rapamycin (mTOR) and lysosome biogenesis genes (TFEB, LAMP-1, LAMP-2, and ATP6V1A). (B) Nuclear fractions from mouse livers of Mboat7fl/fl and Mboat7-HSKO were subjected to western blot analysis of TFEB. (C) Lysosome protein degradation activity in wild-type and MBOAT7∆-Huh7 hepatoma cells treated with or without 100 mM ethanol for 48 hr was assessed by incubating cells with 10 µg/ml of lysosome indicator for 2 hr and examined by flow cytometry. n = 5 from two experiments by normalizing to wild-type group in each experiment; mean ± standard deviation (SD) (D) Expression levels of the genes encoding functions in lysosomal hydrolase and accessory, lysosomal m involved in lysosomal biogenesis in the liver of Mboat7fl/fl and Mboat7-HSKO mice upon ethanol feeding. mRNA expression levels were determined by qPCR (n = 6/group). Groups not sharing a common letter superscript differ significantly (p ≤ 0.05).

Figure 4—source data 1

Original file for the western blot analysis in Figure 4A (anti-LC3-A/B).

https://cdn.elifesciences.org/articles/92243/elife-92243-fig4-data1-v1.zip
Figure 4—source data 2

Original file for the western blot analysis in Figure 4A (anti-p62).

https://cdn.elifesciences.org/articles/92243/elife-92243-fig4-data2-v1.zip
Figure 4—source data 3

Original file for the western blot analysis in Figure 4A (anti-Total mTOR and anti-LAMP-1).

https://cdn.elifesciences.org/articles/92243/elife-92243-fig4-data3-v1.zip
Figure 4—source data 4

Original file for the western blot analysis in Figure 4A (anti-TFEB).

https://cdn.elifesciences.org/articles/92243/elife-92243-fig4-data4-v1.zip
Figure 4—source data 5

Original file for the western blot analysis in Figure 4A (anti-LAMP-2 and anti-ATP6V1A).

https://cdn.elifesciences.org/articles/92243/elife-92243-fig4-data5-v1.zip
Figure 4—source data 6

Original file for the western blot analysis in Figure 4A (anti-β-actin).

https://cdn.elifesciences.org/articles/92243/elife-92243-fig4-data6-v1.zip
Figure 4—source data 7

PDF containing Figure 4A and original scans of the relevant western blot analysis (anti-MBOAT7, anti-p62, anti-total mTOR, anti-total TFEB, anti-LAMP-1, anti-LAMP-2, anti-ATP6V1A, and anti-β-actin) with highlighted bands and sample labels.

https://cdn.elifesciences.org/articles/92243/elife-92243-fig4-data7-v1.zip
Figure 4—source data 8

Original file for the western blot analysis in Figure 4B (anti-cytosolic-TFEB).

https://cdn.elifesciences.org/articles/92243/elife-92243-fig4-data8-v1.zip
Figure 4—source data 9

Original file for the western blot analysis in Figure 4B (anti-nuclear TFEB).

https://cdn.elifesciences.org/articles/92243/elife-92243-fig4-data9-v1.zip
Figure 4—source data 10

Original file for the western blot analysis in Figure 4B (anti-LAMIN A/C).

https://cdn.elifesciences.org/articles/92243/elife-92243-fig4-data10-v1.zip
Figure 4—source data 11

Original file for the western blot analysis in Figure 4B (anti-GAPDH).

https://cdn.elifesciences.org/articles/92243/elife-92243-fig4-data11-v1.zip
Figure 4—source data 12

PDF containing Figure 4B and original scans of the relevant western blot analysis (anti-cytosolic TFEB, anti-nuclear TFEB, anti-LAMIN A/C, and anti-GAPDH) with highlighted bands and sample labels.

https://cdn.elifesciences.org/articles/92243/elife-92243-fig4-data12-v1.zip

We next investigated the cell autonomous effects of ethanol on wild-type human Huh7 hepatoma cells or Huh7 cells genetically lacking MBOAT7 (Huh7-Δ MBOAT7). In agreement with what we found in mouse liver, MBOAT7∆-Huh7 cells had reduced levels of total TFEB and lysosomal marker proteins including LAMP1-, LAMP-2, and ATP6V1A (Figure 4—figure supplement 1). Furthermore, MBOAT7∆-Huh7 cells had increased levels of LC3 and total mTOR, which was particularly apparent upon ethanol exposure (Figure 4—figure supplement 1). We next assessed lysosome protein degradation activity in wild-type and MBOAT7∆-Huh7 hepatoma cells by measuring the degradation of an exogenous lysosome indicator, quantified via median Bodipy/Alexa647 fluorescence ratio. In the absence of ethanol treatment, there was no significant difference in the lysosomal activity between wild-type and MBOAT7∆-Huh7 cells. Lysosome activity was increased by 42% in wild-type cells with ethanol treatment vs. no treatment (p < 0.001, Figure 4C), in agreement with increased mRNA levels of several lysosomal hydrolases (Figure 4D). However, the lysosome activity was decreased by 45% in MBOAT7∆-Huh7 vs. wild-type cells treated with ethanol (p < 0.001, Figure 4C). Collectively, in the presence of EtOH, deletion of MBOAT7 in mouse or human hepatocytes results in defective TFEB-mediated lysosomal biogenesis and lysosome activity, which would be expected to lead to impaired autophagic flux.

Discussion

This manuscript builds on our initial observation that ASO-mediated knockdown of Mboat7 promotes NAFLD progression, hyperinsulinemia, and insulin resistance in mice (Gwag et al., 2019). Here, we have further clarified the cell autonomous roles of Mboat7 in ethanol-driven liver injury by comparing metabolic phenotypes in hepatocyte-specific (Mboat7-HSKO) and myeloid-specific (Mboat7-MSKO) mice. The major findings of the current study include the following: (1) MBOAT7 product PI species (PI 38:4 and PI 38:5) are reduced in the circulation of human consuming high levels of alcohol; (2) MBOAT7 loss of function in hepatocytes, but not myeloid cells, promotes ethanol-induced liver injury in mice; (3) hepatocyte-specific deletion of Mboat7 results in expected alterations in substrate LPI and product PI lipids, but unexpectedly alters lysosomal/endosome BMP lipids in an ethanol-driven manner; (4) genetic deletion in mouse or human hepatocytes results in dysregulation of lysosomal biogenesis and autophagic flux, particularly under ethanol-challenged conditions. This work provides new insights into how genetic variation in MBOAT7 may impact ALD progression in humans and mice. Importantly, this work is the first to causally link MBOAT7 loss of function in hepatocytes, but not myeloid cells, to ethanol-induced liver injury via dysregulation of lysosomal biogenesis and autophagic flux. Although not all human studies agree there is a uniform association between the rs641738 SNP with ALD, our work indicates a very powerful interaction between MBOAT7 loss of function and ethanol-induced liver injury.

Here, we have identified a striking reorganization of the hepatic lipidome in Mboat7-HSKO mice when exposed to ethanol (Figure 3; Figure 3—figure supplements 111). In previous independent studies examining lipid alterations in Mboat7-HSKO mice under experimental conditions to elicit NAFLD and fibrosis (i.e., high fat diets or methionine/choline-deficient diets), the global lipidomic alterations in the liver were much more confined to inositol-containing phospholipids and triacylglycerols (Tanaka et al., 2021; Thangapandi et al., 2021). It is important to note that the work of Xia and colleagues did previously report increased levels on PG lipids specifically in isolated endoplasmic reticulum (ER) membranes from Mboat7-HSKO mice, but our work confirms and extends this to show that not only PG but precursor BMPs are significantly elevated in Mboat7-HSKO mice, particularly when challenged with ethanol. It is still unclear how MBOAT7 impacts endosomal/lysosomal BMP and mitochondrial lipids such as PG and CL under ethanol-exposed conditions, but our work clearly indicates that ethanol reorganizes the global liver lipidome in a MBOAT7-dependent manner. It is most likely that the accumulation of BMP and PG lipids seen in ethanol-challenged Mboat7-HSKO mice are not directly related to the lysophospholipid acyltransferase activity of MBOAT7. Instead, it is more plausible that the accumulation of BMPs and PGs seen in Mboat7-HSKO mice is secondary to indirect reorganization of the arachidonate PI cycle or other related lipid metabolic pathways coordinated at the ER.

For instance, the arachidonate PI cycle is initiated in the ER where inositol is added to (cytidine diphosphate)CDP-diacylglycerol (18:0/20:4) by phosphatidylinositol synthase (PIS) to produce the exact same metabolic product of MBOAT7 (PI 38:4). It is likely that both PIS-generated as well as MBOAT7-generated PI 38:4 can serve as a substrate for PI kinases to form the key second messengers known as PI phosphates [PIPs including PI(18:0/20:4)-4P, PI(18:0/20:4)-4,5P2] and related lipid mediators downstream of phospholipase C in the arachidonate PI cycle IP3, DAG(18:0/20:4), PA(18:0/20:4), and CDP-DAG(18:0/20:4). It is important to note that seminal work by Anderson et al., 2013 found that total PIPs, PI(18:0/20:4)-4P, and PI(18:0/20:4)-4,5P2 were significantly reduced in global Mboat7−/− mice. In fact, more than 85% of PIP species in cultured cells have an sn-1 18:0 and sn-2 20:4 acyl chain composition (i.e., in part originate from the MBOAT7 and PIS product PI 38:4) (Clark et al., 2011; Rouzer et al., 2007). These reductions in PIPs seen with Mboat7 deficiency (Anderson et al., 2013) could have important consequences in cellular signal transduction, given that PIPs are common second messengers generated downstream of ligand activation of numerous receptor systems including hormone, growth factor, cytokine, and chemokine receptors (Wymann and Schneiter, 2008; Pemberton et al., 2020; Fruman et al., 2017; Hoxhaj and Manning, 2020). PIPs also play diverse roles in shaping protein–lipid interactions, membrane fusion events, vesicular transport, solute channel function, and cytoskeletal arrangement (Wymann and Schneiter, 2008; Pemberton et al., 2020; Fruman et al., 2017; Hoxhaj and Manning, 2020). Most relevant to this work, anionic lipids play very important roles in controlling membrane dynamics that shape nearly all steps of autophagy including initiation of autophagosome biogenesis and autophagosome–lysosome fusion (Baba and Balla, 2020; Schink et al., 2016). Collectively, given the fact that MBOAT7 generates the most abundant species of PI (PI 38:4), and key cellular PIPs [PI(18:0/20:4)-4P and PI(18:0/20:4)-4,5P2], there is a strong potential that the primary alterations in PI and PIP lipids could broadly alter cellular signal transduction, endosomal/lysosomal lipid sorting, membrane fusion events, vesicular transport, solute channel function, cytoskeletal arrangement, and autophagic flux.

Collectively, this work provides new cellular and molecular insights into how genetic variation in MBOAT7 impacts ALD progression in humans and mice. This work is the first to causally link MBOAT7 loss of function in hepatocytes, but not myeloid cells, to ethanol-induced liver injury via dysregulation of lysosomal biogenesis and autophagic flux and broaden our understanding of the lipid metabolic mechanisms promoting ethanol-induced liver injury. This work also shows that MBOAT7-driven LPI acylation in the ER can indirectly impact both lysosomal (BMP) and mitochondrial (CL and PG) lipids which can have broad impacts on autophagy described here, as well as defective fatty acid oxidation as we originally reported in high fat diet-fed mice (Gwag et al., 2019). The results of this work have broad potential implications in the management of both alcoholic- and non-alcoholic fatty liver disease, indicating that strategies that effectively restore both lysosomal and mitochondrial function may hold some therapeutic promise in humans with the common MBOAT7 rs641738 variant.

Materials and methods

Key resources table
Reagent type (species) or resourceDesignationSource or referenceIdentifiersAdditional information
Genetic reagent (M. musculus)Mboat7tm1a(KOMP)Wtsi/Mboat7tm1a(KOMP)WtsiPMID:23472195RRID: MGI:5510874
Genetic reagent (M. musculus)B6N.Cg-Speer6-ps1Tg(Alb-cre)21Mgn/JJackson LaboratoryStock#: 018961
RRID: IMSR_JAX:018961
Genetic reagent (M. musculus)B6.129P2-Lyz2tm1(cre)Ifo/JJackson LaboratoryStock#: 004781
RRID: IMSR_JAX:004781
Cell line (Homo sapiens)HUH7 (well differentiated human hepatocellular carcinoma)Japanese Collection of Research Biosources Cell BankJCRB0403
AntibodyAnti-MBOAT7 (Rat monoclonal)PMID:23097495RRID: AB_2813851WB (1:1000)
AntibodyAnti-rat IgG HRP secondary antibodyCell SignalingCat#: 7077
RRID: AB_10694715
WB (1:5000)
AntibodyLC3A/B (D3U4C) XP (Rabbit monoclonal)Cell SignalingCat#: 12741
RRID: AB_2617131
WB (1:1000)
AntibodySQSTM1/p62 (Rabbit polyclonal)Cell SignalingCat#: 5114
RRID: AB_10624872
WB (1:1000)
AntibodymTOR (7C10) (Rabbit monoclonal)Cell SignalingCat#: 2983
RRID: AB_2105622
WB (1:1000)
AntibodyTFEB (D2O7D) (Rabbit monoclonal)Cell SignalingCat#: 37785WB (1:1000)
AntibodyTFEB (Rabbit polyclonal)Thermo Fisher ScientificCat#: A303-673A
RRID: AB_11204751
WB (1:1000)
AntibodyLAMP1 (D2D11) XP Rabbit monoclonalCell SignalingCat#: 9091
RRID: AB_2687579
WB (1:1000)
AntibodyLAMP-1 (Rat monoclonal)Developmental Studies Hybridoma Bank (DSHB)Cat#: ID4B
RRID: AB_528127
WB (1:1000)
AntibodyLAMP-2 (Rat monoclonal)Developmental Studies Hybridoma Bank (DSHB)Cat#: ABL-93
RRID: AB_2134767
WB (1:1000)
AntibodyLAMP2 (D5C2P) (Rabbit monoclonal)Cell SignalingCat#: 49067
RRID: AB_2799349
WB (1:1000)
AntibodyATP6V1A (Rabbit polyclonal)GeneTexCat#: GTX110815
RRID: AB_1949704
WB (1:1000)
antibodyAnti-GAPDH-HRP (Rabbit monoclonal)Cell SignalingCat#: 8884
RRID: AB_11129865
WB (1:5000)
AntibodyLamin A/C (4C11) (Mouse monoclonal)Cell SignalingCat#: 4777WB (1:1000)
AntibodyHRP-conjugated Beta Actin (Mouse monoclonal)ProteintechCat#: HRP-60008
RRID: AB_2289225
WB (1:10,000)
Commercial assay or kitAlanine Aminotransaminase (ALT) kitSekisui Diagnostics318-30
Commercial assay or kitAspartate Aminotransferase (AST) kitSekisui Diagnostics319-30
Commercial assay or kitLiver TriglycerideWako994-02891
Commercial assay or kitMicrosome IsolationAbcamab206995
Commercial assay or kitNE-PER Nuclear and Cytoplasmic Extraction ReagentsThermo Fisher
Scientific
78833
Commercial assay or kitSupersignal West Pico Plus substrateThermo Fisher
Scientific
34577
Chemical compound, drugAmmonium formateHoneywellCat# 55674
Chemical compound, drugMethanolHoneywellCat# LC230-4
Chemical compound, drugWaterHoneywellCat# LC365-4
Chemical compound, drugAcetonitrileHoneywellCat# LC015-4
Chemical compound, drugIsopropanolFisher ScientificCat# A461-4
Chemical compound, drugEthyl acetateSigma-AldrichCat# 650528
Chemical compound, drugFormic acidThermo ScientificCat# 28905
Chemical compound, drugFA 18:0d35Cayman ChemicalCat# 9003318
Chemical compound, drugACar 18:1d3Cayman ChemicalCat# 26578
Chemical compound, drugBMP 14:0_14:0AvantiCat# 857131
Chemical compound, drugPG 15:0_18:1d7AvantiCat# 91640
Chemical compound, drugCer d18:1d7_15:0AvantiCat# 860681P
Chemical compound, drugPA 15:0_18:1d7AvantiCat# 791642
Chemical compound, drugSPLASH LipidoMix IIAvantiCat# 330709
Chemical compound, drugBMP 18:1_18:1AvantiCat# 857133P
Chemical compound, drugPG 18:1_18:1AvantiCat# 840475P

Human studies

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Healthy Control and Heavy Drinking Patient Selection – Healthy controls or heavy drinkers with an AUDIT score greater than >16 were recruited from the Clinical Research Unit at the Cleveland Clinic or MetroHealth Hospital in Cleveland, Ohio based on medical history and physical examination. The study protocol was approved by the Institutional Review Board for the Protection of Human Subjects in Research at the Cleveland Clinic (IRB 17-718) and MetroHealth Hospital Cleveland (IRB 18-00911). All methods were performed in accordance with the internal review board’s guidelines and regulations, and written, informed consent was obtained from all subjects. Subject demographics are shown in Figure 1—source data 1.

Mice and experimental diets

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To generate conditional Mboat7 knockout mice, we obtained ‘knockout first’ (Mboat7tm1a(KOMP)Wtsi) mice from Dr. Philip Hawkins (Anderson et al., 2013), and crossed these mice with mice transgenically expressing FLP recombinase to remove the NEO cassette resulting in a conditional Mboat7 floxed allele. The FLP transgene was then subsequently bred out of the line and resulting Mboat7flox/WT mice, which were used to expand further downstream tissue-specific knockout lines. To generate congenic hepatocyte-specific (Mboat7-HSKO) and myeloid-specific (Mboat7-MSKO) Mboat7 knockout mice we crossed mice harboring a post-FLP recombinase conditionally targeted Mboat7 floxed allele (Anderson et al., 2013; Massey et al., 2023) to mice transgenically expressing Cre recombinase under the albumin promoter/enhancer (Postic et al., 1999) or Cre knocked into the M lysozyme locus (Clausen et al., 1999), respectively. These independent Mboat7-HSKO and Mboat7-MSKO lines were then backcrossed mice >10 generations into the C57BL/6J background and subsequently subjected to ethanol exposure. Confirmation of sufficient backcrossing into the C57BL/6J background was confirmed by mouse genome SNP scanning at the Jackson Laboratory (Bar Harbor, ME). Age- and weight-matched female (8–10 weeks old) control (Mboat7flox/flox), hepatocyte-specific Mboat7 knockout mice (Mboat7-HSKO), or myeloid-specific Mboat7 knockout mice (Mboat7-MSKO) were maintained on a chow diet and randomized into pair- and ethanol-fed groups using the NIAAA model (Bertola et al., 2013). Briefly, mice were initially fed with control Lieber–DeCarli diet ad libitum for 5 days to acclimatize them to liquid diet. Afterward, ethanol (EtOH)-fed groups were allowed free access to the ethanol Lieber–DeCarli diet containing 5% (vol/vol) ethanol for 10 days, and control groups were pair-fed with the isocaloric substituted maltose dextrins as control diet. At day 11, ethanol- and pair-fed mice were gavaged in the early morning with a single dose of ethanol (5 g/kg body weight) or isocaloric maltose dextrin, respectively, and euthanized 6 hr later (Bertola et al., 2013). All mice were maintained in an Association for the Assessment and Accreditation of Laboratory Animal Care, International-approved animal facility, and all experimental protocols were approved by the Institutional Animal Care and Use Committee of the Cleveland Clinic (IACUC protocols # 2018-2053 and # 00002499).

Histological analysis and imaging

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Hematoxylin and eosin (H&E) staining of paraffin-embedded liver sections was performed as previously described (Gromovsky et al., 2017; Warrier et al., 2015; Brown et al., 2010; Brown et al., 2008a; Brown et al., 2008b). Histopathologic evaluation was scored in a blinded fashion by a board-certified pathologist with expertise in gastrointestinal/liver pathology (Daniela S. Allende – Cleveland Clinic). H&E slides were scanned using a Leica Aperio AT2 Slide Scanner (Leica Microsystems, GmbH, Wetzlar, Germany) and images were processed using ImageScope (Aperio, Software Version 12.1).

Immunoblotting

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Whole tissue homogenates were made from tissues in a modified RIPA buffer as previously described (Warrier et al., 2015; Gwag et al., 2019; Schugar et al., 2017; Lord et al., 2016), microsome was isolated from the livers of control (Mboat7fl/fl) or hepatocyte-specific Mboat7 knockout mice (Mboat7-HSKO) using microsome isolation kit from Abcam and protein was quantified using the bicinchoninic assay (Pierce). Proteins were separated by 4–12% sodium dodecyl sulfate–polyacrylamide gel electrophoresis transferred to polyvinylidene difluoride membranes, and then proteins were detected after incubation with specific antibodies as previously described (Warrier et al., 2015; Gwag et al., 2019; Schugar et al., 2017; Lord et al., 2016) and listed in the Key resources table.

Real-time PCR analysis of gene expression

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Tissue RNA extraction and qPCR analysis were performed as previously described (Gwag et al., 2019). The mRNA expression levels were calculated based on the ΔΔCT method using cyclophilin A as the housekeeping gene. qPCR was conducted using the Applied Biosystems 7500 Real-Time PCRsystem. All primer sequences can be found in Supplementary file 1.

Plasma and liver biochemistries

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To determine the level of hepatic injury in mice fed HFD, plasma was used to analyze ALT levels using enzymatic assays as previously described (Gwag et al., 2019). Extraction of liver lipids and quantification of total plasma and hepatic triglycerides were conducted using enzymatic assays as described previously (Gwag et al., 2019; Gromovsky et al., 2017).

Liver lipid extraction

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Lipids were extracted from liver samples as previously described (Jain et al., 2022). Samples were kept on ice throughout the extraction. Briefly, liver was homogenized in 500 µl of 3:1:6 isopropanol:water:ethyl acetate containing internal standard in ceramic bead tubes (QIAGEN #13113-50) using the TissueLyzer II (QIAGEN #9244420). Samples were centrifuged at 16,000 × g for 10 min at 4°C and the lipid containing supernatant was transferred to a new 1.5 ml tube. Lipid extracts were dried in a SpeedVac (Thermo Savant RVT5105) and resuspended in 150 µl of methanol. Samples were kept at 4°C for no more than 1 week before analysis.

Targeted quantification of LPI and PI lipids

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Quantitation of LPI and PI species was performed as previously described (Gwag et al., 2019) Briefly, LPI and PI standards (LPI 16:0, LPI 18:0, LPI 18:1, LPI 20:4, PI 38:4) and the two internal standards (LPI 17:1-d31, PI 34:1-d31) were purchased Avanti Polar Lipids. High-performance liquid chromatography (HPLC) grade water, methanol, and acetonitrile were purchased from Thermo Fisher Scientific. Standard LPI and PI species at concentrations of 0, 5, 20, 100, 500, and 2000 ng/ml were prepared in 90% methanol containing two internal standards at the concentration of 500 ng/ml. Samples were injected into the Shimadzu LCMS-8050 for generating the internal standard calibration curves. A triple quadrupole mass spectrometer (Quantiva, Thermos Fisher Scientific, Waltham, MA, USA) was used for analysis of LPI and PI species. The mass spectrometer was coupled to the outlet of an UHPLC system (Vanquish, Thermos Fisher Scientific, Waltham, MA, USA), including an auto sampler with refrigerated sample compartment and inline vacuum degasser. The HPLC eluent was directly injected into the triple quadrupole mass spectrometer and the analytes were ionized at ESI(Electrospray Ionization) negative mode. Analytes were quantified using Selected Reaction Monitoring (SRM) and the SRM transitions (m/z) were 571 → 255 for LPI 16:0, 599 → 283 for LPI 18:0, 597 → 281 for LPI 18:1, 619 → 303 for LPI 20:4, 885 → 241 for PI 38:4, 583 → 267 for internal standard LPI 17:1, and 866 → 281 for internal standard PI 34:1-d31. Xcalibur software was used to get the peak area for both the internal standards and LPI and PI species. The internal standard calibration curves were used to calculate the concentration of LPI and PI species in the samples.

Untargeted lipidomics

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Untargeted lipidomics was performed using an Agilent 1290 Infinity II liquid chromatograph equipped with a Waters Acquity BEH C18 column (1.7 μm 2.1 × 100 mm) coupled to an Agilent 6546 Q-TOF mass spectrometer. Mobile phase A was 60:40 acetonitrile:water and B was 90:9:1 isopropanol:acetonitrile:water with both phases buffered with 10 mM ammonium formate and 0.1% formic acid. The gradient was as follows: starting at 15% B to 30% B at 2.40 min, 48% at 3 min, 82% at 13.2 min, 99% at 13.8 min then held at 99% until 15.4 min before equilibrating 15%, held until 20 min. Samples were analyzed in both positive and negative ionizations in separate experiments with the following MS parameters: drying gas flowing 12 l/min at 250°C, nebulizer at 30 psi and sheath gas flowing 11 l/min at 300°C for positive mode. The sheath gas flow was 12 l/min at 375°C with a nebulizer pressure of 30 psi in negative mode. Both ionizations had the same voltage for capillary (4000 V), skimmer (75 V), fragmentor (190 V), and octupole (750 V). Reference masses (m/z = 121.05 and 922 for positive mode; 112.98 and 966.00 for negative mode) were continuously infused during sample runs for accurate mass calibration. Pooled samples were run in consecutive iterative MS/MS injections at a constant collision energy of 25 V to collect spectra for lipid library creation using Agilent LipidAnnotator and MS1 data were collected for all individual samples. Peak integration was performed using Agilent Profinder (v8.0) software and further curation and internal standard normalization were performed using in-house R scripts as described previously (Jain et al., 2022). Untargeted data are reported in units of pmol lipid/g tissue. Principal component analysis and heatmaps were generated using MetaboAnalyst (Pang et al., 2021). The first and second principal components are plotted on the x- and y-axis, respectively, and sample treatment group is indicated by color. For heatmap generation, data were pareto-scaled and the normalized intensity is indicated by color with relative increase in red and decrease in blue. The top 70 lipid features by analysis of variance (ANOVA) p-value are plotted on the y-axis and samples are grouped by Ward clustering on the x-axis. Sample treatments are distinguished by color.

Targeted quantification of BMPs and PGs

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Targeted BMP and PG lipid analysis was conducted on an Agilent 1290 Infinity II LC coupled to an Agilent 6495C QQQ mass spectrometer. The same column, mobile phases, and gradient were used for targeted analysis as for the untargeted. The MS parameters for both ionizations were as follows: drying gas temp flow of 12 l/min at 250°C with nebulizer at 35 psi and sheath gas flow of 11 l/min at 300°C. Capillary voltage was kept at 4000 V with nozzle voltage of 500 V and collision energy = 20 V. The iFunnel high pressure RF was at 150/90 V and low-pressure RF at 60/60 V for positive/negative ionizations, respectively. A sample type specific lipid library was created by running pooled liver extract multiple times in positive ionization using multiple reaction monitoring (MRM) to scan for a list of BMP/PG lipid precursor masses ([NH4]+ adducts) with transitions for the respective fatty acid and diacylglycerol (−17 m/z) adducts (Grabner et al., 2019). BMPs were identified by the characteristic free fatty acid fragment being much higher intensity than the diacylglycerol fragment, whereas the opposite was true for PG. In addition, BMP lipids eluted between 0.3 and 0.5 min earlier than the isomeric PG counterpart. These distinguishing characteristics were validated using commercial BMP 18:1_18:1 and PG 18:1_18:1 standards. A dynamic MRM method was created using the retention time data gathered from positive ionization tests but in negative mode scanning for the [M−H] precursors and fatty acid fragments as BMP and PG are much more readily ionized in negative mode. Data were integrated in Agilent Quantitative analysis and peaks were manually adjusted and verified. Final units are in ng lipid/mg tissue.

Lysosome protein degradation activity assay

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Lysosome protein degradation activity was performed as previously described (Robinet et al., 2021). Briefly, DQ-ovalbumin (D12053, Thermo Fisher Scientific) was labeled with Alexa Fluor 647 succinimidyl ester (A20006, Thermo Fisher Scientific) at room temperature for 1 hr (3:1 dye:protein mole ratio) to make lysosome protein degradation indicator. The reaction was stopped by incubating the conjugate with 1.5 M hydroxylamine (pH 8.5) for 1 hr at room temperature, and the conjugate was purified by extensive dialysis. Lysosome protein degradation indicator was validated in vitro by incubating with proteinase K and achieving significant increase of Bodipy/Alexa647 ratio (Robinet et al., 2021). The double labeled ovalbumin has increased Bodipy fluorescence upon its degradation by decreasing its self-quenching, while Alexa647 is not changed upon its degradation and is used to normalize for cellular uptake. Wild-type or MBOAT7∆-Huh7 hepatoma cells treated with or without 100 mM ethanol for 48 hr were incubated with 10 µg/ml of lysosome indicator for 2 hr, and lysosome activity was analyzed by flow cytometry in ≈10,000 cells with a LSRFortessa device (BD). Flowjo software was used to export data for each cell for ratiometric analyses, and the median Bodipy/Alexa647 ratio for each independent well was used for analysis.

Statistical analyses

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Single comparisons between two groups were performed using two-tailed Student’s t tests with 95% confidence intervals. Comparisons involving multiple groups beyond binary comparison were assessed using one-way ANOVA with Tukey’s post hoc test. All data presented as mean ± standard error of the mean. Values were considered significant at p < 0.05 (using superscripts), or ***p < 0.001 and ****p < 0.0001 in Figure 1. JMP 17.0 statistical discovery software (SAS Institute, Cary, NC, USA) was used for all statistical analyses.

Data availability

All data generated or analyzed during this study are included in the manuscript and supporting files.

References

Article and author information

Author details

  1. Venkateshwari Varadharajan

    1. Department of Cancer Biology, Lerner Research Institute of the Cleveland Clinic, Cleveland, United States
    2. Center for Microbiome and Human Health, Lerner Research Institute, Cleveland Clinic, Cleveland, United States
    3. Northern Ohio Alcohol Center (NOAC), Lerner Research Institute, Cleveland Clinic, Cleveland, United States
    Contribution
    Conceptualization, Resources, Data curation, Software, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing – original draft, Writing – review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-4557-9840
  2. Iyappan Ramachandiran

    1. Department of Cancer Biology, Lerner Research Institute of the Cleveland Clinic, Cleveland, United States
    2. Center for Microbiome and Human Health, Lerner Research Institute, Cleveland Clinic, Cleveland, United States
    3. Northern Ohio Alcohol Center (NOAC), Lerner Research Institute, Cleveland Clinic, Cleveland, United States
    Contribution
    Data curation, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing – review and editing
    Competing interests
    No competing interests declared
  3. William J Massey

    1. Department of Cancer Biology, Lerner Research Institute of the Cleveland Clinic, Cleveland, United States
    2. Center for Microbiome and Human Health, Lerner Research Institute, Cleveland Clinic, Cleveland, United States
    3. Northern Ohio Alcohol Center (NOAC), Lerner Research Institute, Cleveland Clinic, Cleveland, United States
    Contribution
    Data curation, Investigation, Visualization, Methodology, Writing – review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-2087-6048
  4. Raghav Jain

    Department of Biochemistry, University of Wisconsin-Madison, Madison, United States
    Contribution
    Data curation, Formal analysis, Investigation, Visualization, Methodology, Writing – review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-1284-6762
  5. Rakhee Banerjee

    1. Department of Cancer Biology, Lerner Research Institute of the Cleveland Clinic, Cleveland, United States
    2. Center for Microbiome and Human Health, Lerner Research Institute, Cleveland Clinic, Cleveland, United States
    3. Northern Ohio Alcohol Center (NOAC), Lerner Research Institute, Cleveland Clinic, Cleveland, United States
    Contribution
    Data curation, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing – review and editing
    Competing interests
    No competing interests declared
  6. Anthony J Horak

    1. Department of Cancer Biology, Lerner Research Institute of the Cleveland Clinic, Cleveland, United States
    2. Center for Microbiome and Human Health, Lerner Research Institute, Cleveland Clinic, Cleveland, United States
    3. Northern Ohio Alcohol Center (NOAC), Lerner Research Institute, Cleveland Clinic, Cleveland, United States
    Contribution
    Data curation, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing – review and editing
    Competing interests
    No competing interests declared
  7. Megan R McMullen

    1. Northern Ohio Alcohol Center (NOAC), Lerner Research Institute, Cleveland Clinic, Cleveland, United States
    2. Department of Inflammation and Immunity, Lerner Research Institute, Cleveland Clinic, Cleveland, United States
    Contribution
    Data curation, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing – review and editing
    Competing interests
    No competing interests declared
  8. Emily Huang

    1. Northern Ohio Alcohol Center (NOAC), Lerner Research Institute, Cleveland Clinic, Cleveland, United States
    2. Department of Inflammation and Immunity, Lerner Research Institute, Cleveland Clinic, Cleveland, United States
    Contribution
    Data curation, Validation, Investigation, Visualization, Methodology, Writing – review and editing
    Competing interests
    No competing interests declared
  9. Annette Bellar

    Northern Ohio Alcohol Center (NOAC), Lerner Research Institute, Cleveland Clinic, Cleveland, United States
    Contribution
    Data curation, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing – review and editing
    Competing interests
    No competing interests declared
  10. Shuhui W Lorkowski

    Department of Cardiovascular and Metabolic Sciences, Lerner Research Institute of the Cleveland Clinic, Cleveland, United States
    Contribution
    Data curation, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing – review and editing
    Competing interests
    No competing interests declared
  11. Kailash Gulshan

    Center for Gene Regulation in Health and Disease (GRHD), Cleveland State University, Cleveland, United States
    Contribution
    Data curation, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing – review and editing
    Competing interests
    No competing interests declared
  12. Robert N Helsley

    1. Department of Cancer Biology, Lerner Research Institute of the Cleveland Clinic, Cleveland, United States
    2. Department of Pharmacology & Nutritional Sciences, Saha Cardiovascular Research Center, University of Kentucky College of Medicine, Lexington, United States
    Contribution
    Data curation, Formal analysis, Validation, Investigation, Visualization, Methodology
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-5000-3187
  13. Isabella James

    Department of Biochemistry, University of Wisconsin-Madison, Madison, United States
    Contribution
    Data curation, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing – review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0009-0000-2796-3497
  14. Vai Pathak

    1. Northern Ohio Alcohol Center (NOAC), Lerner Research Institute, Cleveland Clinic, Cleveland, United States
    2. Department of Inflammation and Immunity, Lerner Research Institute, Cleveland Clinic, Cleveland, United States
    Contribution
    Data curation, Formal analysis, Validation, Investigation, Visualization, Methodology
    Competing interests
    No competing interests declared
  15. Jaividhya Dasarathy

    1. Northern Ohio Alcohol Center (NOAC), Lerner Research Institute, Cleveland Clinic, Cleveland, United States
    2. Department of Family Medicine, Metro Health Medical Center, Case Western Reserve University, Cleveland, United States
    Contribution
    Data curation, Formal analysis, Supervision, Validation, Investigation, Visualization, Methodology, Writing – review and editing
    Competing interests
    No competing interests declared
  16. Nicole Welch

    1. Northern Ohio Alcohol Center (NOAC), Lerner Research Institute, Cleveland Clinic, Cleveland, United States
    2. Department of Inflammation and Immunity, Lerner Research Institute, Cleveland Clinic, Cleveland, United States
    Contribution
    Data curation, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing – review and editing
    Competing interests
    No competing interests declared
  17. Srinivasan Dasarathy

    1. Center for Microbiome and Human Health, Lerner Research Institute, Cleveland Clinic, Cleveland, United States
    2. Northern Ohio Alcohol Center (NOAC), Lerner Research Institute, Cleveland Clinic, Cleveland, United States
    3. Department of Inflammation and Immunity, Lerner Research Institute, Cleveland Clinic, Cleveland, United States
    Contribution
    Data curation, Formal analysis, Supervision, Validation, Investigation, Visualization, Methodology, Writing – review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-1774-0104
  18. David Streem

    Lutheran Hospital, Cleveland Clinic, Cleveland, United States
    Contribution
    Data curation, Formal analysis, Supervision, Validation, Investigation, Visualization, Methodology, Writing – review and editing
    Competing interests
    No competing interests declared
  19. Ofer Reizes

    Center for Microbiome and Human Health, Lerner Research Institute, Cleveland Clinic, Cleveland, United States
    Contribution
    Data curation, Formal analysis, Supervision, Validation, Investigation, Visualization, Methodology, Writing – review and editing
    Competing interests
    No competing interests declared
  20. Daniela S Allende

    1. Northern Ohio Alcohol Center (NOAC), Lerner Research Institute, Cleveland Clinic, Cleveland, United States
    2. Department of Anatomical Pathology, Cleveland Clinic, Cleveland, United States
    Contribution
    Data curation, Formal analysis, Supervision, Validation, Investigation, Visualization, Methodology, Writing – review and editing
    Competing interests
    No competing interests declared
  21. Jonathan D Smith

    Department of Cancer Biology, Lerner Research Institute of the Cleveland Clinic, Cleveland, United States
    Contribution
    Data curation, Formal analysis, Supervision, Validation, Investigation, Visualization, Methodology, Writing – review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-0415-386X
  22. Judith Simcox

    Department of Biochemistry, University of Wisconsin-Madison, Madison, United States
    Contribution
    Data curation, Formal analysis, Supervision, Validation, Investigation, Visualization, Methodology, Writing – review and editing
    Competing interests
    Reviewing editor, eLife
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-7350-6342
  23. Laura E Nagy

    1. Center for Microbiome and Human Health, Lerner Research Institute, Cleveland Clinic, Cleveland, United States
    2. Northern Ohio Alcohol Center (NOAC), Lerner Research Institute, Cleveland Clinic, Cleveland, United States
    3. Department of Inflammation and Immunity, Lerner Research Institute, Cleveland Clinic, Cleveland, United States
    Contribution
    Data curation, Formal analysis, Supervision, Validation, Investigation, Visualization, Methodology, Writing – review and editing, helped design experiments and provided useful discussion directing collaborative aspects of the project
    Competing interests
    No competing interests declared
  24. J Mark Brown

    1. Center for Microbiome and Human Health, Lerner Research Institute, Cleveland Clinic, Cleveland, United States
    2. Northern Ohio Alcohol Center (NOAC), Lerner Research Institute, Cleveland Clinic, Cleveland, United States
    Contribution
    Conceptualization, Resources, Data curation, Software, Formal analysis, Supervision, Funding acquisition, Validation, Investigation, Visualization, Methodology, Writing – original draft, Project administration, Writing – review and editing
    For correspondence
    brownm5@ccf.org
    Competing interests
    Reviewing editor, eLife
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-2708-7487

Funding

NIH Office of the Director (P50 AA024333)

  • Srinivasan Dasarathy
  • Daniela S Allende
  • Jonathan D Smith
  • Laura E Nagy
  • J Mark Brown

NIH Office of the Director (R01 DK120679)

  • J Mark Brown

NIH Office of the Director (RF1 NS133812)

  • J Mark Brown

NIH Office of the Director (P01 HL147823)

  • J Mark Brown

NIH Office of the Director (U01 AA026938)

  • Laura E Nagy
  • J Mark Brown

NIH Office of the Director (R01 DK130227)

  • J Mark Brown

NIH Office of the Director (U01 AA026264)

  • J Mark Brown

NIH Office of the Director (R01 HL128268)

  • Jonathan D Smith

JDRF (JDRF201309442)

  • Judith Simcox

Glenn Foundation for Medical Research (A22068)

  • Judith Simcox

NIH Office of the Director (R01 DK133479)

  • Judith Simcox

University of Wisconsin-Madison (WIS04000)

  • Raghav Jain
  • Judith Simcox

NIH Office of the Director (DK128022)

  • Robert N Helsley

University of Kentucky (UL1TR001998)

  • Robert N Helsley

American Heart Association (19POST34380725)

  • Iyappan Ramachandiran

The funders had no role in study design, data collection, and interpretation, or the decision to submit the work for publication.

Acknowledgements

This work was supported in part by National Institutes of Health grants P50 AA024333 (J.D., S.D., D.S.A., L.E.N., J.M.B.), R01 DK120679 (J.M.B.), RF1 NS133812 (J.M.B.), P01 HL147823 (J.M.B.), U01 AA026938 (L.E.N., J.M.B.), R01 DK130227 (J.M.B.), U01 AA026264 (L.E.N.), R01 HL128268 (J.D.S.), JDRF JDRF201309442 (J.S.), the Glenn and AFAR Junior Faculty Grant A22068 (J.S.), R01 DK133479 (J.S.); Hatch Grant WIS04000 (R.J., J.S.), K01 DK128022 (R.N.H.), UL1TR001998 (R.N.H.), and the American Heart Association-Postdoctoral Fellowships 19POST34380725 (I.R.). The authors would like to thank Dr. Philip Hawkins (Babraham Institute) for providing Mboat7 knockout first mice.

Ethics

The study protocol was approved by the Institutional Review Board for the Protection of Human Subjects in Research at the Cleveland Clinic (IRB 17-718) and MetroHealth Hospital Cleveland (IRB 18-00911). All methods were performed in accordance with the internal review board's guidelines and regulations, and written, informed consent was obtained from all subjects.

All mice were maintained in an Association for the Assessment and Accreditation of Laboratory Animal Care, International-approved animal facility, and all experimental protocols were approved by the Institutional Animal Care and Use Committee of the Cleveland Clinic (IACUC protocols # 2018-2053 and # 00002499).

Version history

  1. Sent for peer review: September 11, 2023
  2. Preprint posted: September 27, 2023 (view preprint)
  3. Preprint posted: December 27, 2023 (view preprint)
  4. Preprint posted: March 4, 2024 (view preprint)
  5. Version of Record published: April 22, 2024 (version 1)

Cite all versions

You can cite all versions using the DOI https://doi.org/10.7554/eLife.92243. This DOI represents all versions, and will always resolve to the latest one.

Copyright

© 2023, Varadharajan et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

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  1. Venkateshwari Varadharajan
  2. Iyappan Ramachandiran
  3. William J Massey
  4. Raghav Jain
  5. Rakhee Banerjee
  6. Anthony J Horak
  7. Megan R McMullen
  8. Emily Huang
  9. Annette Bellar
  10. Shuhui W Lorkowski
  11. Kailash Gulshan
  12. Robert N Helsley
  13. Isabella James
  14. Vai Pathak
  15. Jaividhya Dasarathy
  16. Nicole Welch
  17. Srinivasan Dasarathy
  18. David Streem
  19. Ofer Reizes
  20. Daniela S Allende
  21. Jonathan D Smith
  22. Judith Simcox
  23. Laura E Nagy
  24. J Mark Brown
(2024)
Membrane-bound O-acyltransferase 7 (MBOAT7) shapes lysosomal lipid homeostasis and function to control alcohol-associated liver injury
eLife 12:RP92243.
https://doi.org/10.7554/eLife.92243.3

Share this article

https://doi.org/10.7554/eLife.92243

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