Very long-chain ceramides in muscle associate with insulin resistance independent of obesity

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Anna Howell , Zora Modrusan , View ORCID Profile Anthony S Don , Jacqueline Stöckli , View ORCID Profile Alexis Diaz Vegas , View ORCID Profile David E James doi: https://doi.org/10.1101/2025.03.04.641442 Søren Madsen 1 School of Life and Environmental Sciences, University of Sydney , Camperdown, New South Wales, Australia 2 Charles Perkins Centre, University of Sydney , Camperdown, New South Wales, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Søren Madsen For correspondence: soren.madsen{at}sydney.eu.au alexis.diaz{at}sydney.edu.au david.james{at}sydney.edu.au Harry B Cutler 1 School of Life and Environmental Sciences, University of Sydney , Camperdown, New South Wales, Australia 2 Charles Perkins Centre, University of Sydney , Camperdown, New South Wales, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Harry B Cutler Kristen C Cooke 1 School of Life and Environmental Sciences, University of Sydney , Camperdown, New South Wales, Australia 2 Charles Perkins Centre, University of Sydney , Camperdown, New South Wales, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site Meg Potter 1 School of Life and Environmental Sciences, University of Sydney , Camperdown, New South Wales, Australia 2 Charles Perkins Centre, University of Sydney , Camperdown, New South Wales, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site Jasmine Khor 2 Charles Perkins Centre, University of Sydney , Camperdown, New South Wales, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site Christoph D Rau 3 Computational Medicine Program and Department of Human Genetics, University of North Carolina at Chapel Hill , United States Find this author on Google Scholar Find this author on PubMed Search for this author on this site Stewart WC Masson 1 School of Life and Environmental Sciences, University of Sydney , Camperdown, New South Wales, Australia 2 Charles Perkins Centre, University of Sydney , Camperdown, New South Wales, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Stewart WC Masson Anna Howell 1 School of Life and Environmental Sciences, University of Sydney , Camperdown, New South Wales, Australia 2 Charles Perkins Centre, University of Sydney , Camperdown, New South Wales, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site Zora Modrusan 4 Department of Microchemistry , Proteomics, and Lipidomics, Genentech, South San Francisco, United States Find this author on Google Scholar Find this author on PubMed Search for this author on this site Anthony S Don 2 Charles Perkins Centre, University of Sydney , Camperdown, New South Wales, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Anthony S Don Jacqueline Stöckli 1 School of Life and Environmental Sciences, University of Sydney , Camperdown, New South Wales, Australia 2 Charles Perkins Centre, University of Sydney , Camperdown, New South Wales, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site Alexis Diaz Vegas 1 School of Life and Environmental Sciences, University of Sydney , Camperdown, New South Wales, Australia 2 Charles Perkins Centre, University of Sydney , Camperdown, New South Wales, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Alexis Diaz Vegas For correspondence: soren.madsen{at}sydney.eu.au alexis.diaz{at}sydney.edu.au david.james{at}sydney.edu.au David E James 1 School of Life and Environmental Sciences, University of Sydney , Camperdown, New South Wales, Australia 2 Charles Perkins Centre, University of Sydney , Camperdown, New South Wales, Australia 5 School of Medical Sciences, University of Sydney , Camperdown, New South Wales, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for David E James For correspondence: soren.madsen{at}sydney.eu.au alexis.diaz{at}sydney.edu.au david.james{at}sydney.edu.au Abstract Full Text Info/History Metrics Preview PDF Abstract Lipids, in particular ceramides and diacylglycerols (DAGs), are implicated in insulin resistance, however their precise roles remain unclear. We leverage natural genetic variation to examine muscle lipids and systemic insulin resistance (IR) in 399 Diversity Outbred Australia mice. Adipose mass was associated with 55% of muscle lipids and IR, with DAGs as the only enriched lipid class. To disentangle adiposity and muscle lipid contributions, we used two approaches: (1) linear modelling of muscle lipids corrected for adipose mass on systemic IR, and (2) stratifying mice into insulin sensitivity quartiles within adiposity bins. Both revealed that very long-chain ceramides, but not DAGs, were linked to IR. Transcriptomic and proteomics further associated these ceramides with cellular and mitochondrial stress. DAGs correlated with leptin expression in muscle, suggesting they originate from muscle-residing adipocytes. We propose that many muscle lipids, including DAGs, associate with IR due to adipose accumulation rather than directly influencing muscle insulin sensitivity. By addressing the relationship between adiposity and metabolic state, we identified very long-chain muscle ceramides as highly associated with IR independently of adiposity. Introduction Insulin resistance (IR), defined by a defect in insulin-regulated glucose metabolism, is one of the earliest risk factors for chronic cardiometabolic disorders and is shaped by both genetic and environmental factors ( James et al , 2021 ). At a systemic level, IR triggers a compensatory increase in insulin secretion from pancreatic β-cells, which helps maintain stable blood glucose levels despite the presence of IR ( James et al , 2021 ). This compensatory hyperinsulinemia has been associated with several pathologies, including cancer, heart disease, and metabolic associated fatty liver disease ( Zhang et al , 2023 ; Steneberg et al , 2015 ; Després et al , 1996 ). Skeletal muscle plays a crucial role in systemic insulin sensitivity, as it is the primary site for postprandial carbohydrate uptake ( Thiebaud et al , 1982 ). Moreover, increased muscle insulin sensitivity is sufficient to protect mice against diet-induced metabolic dysfunction, such as fasting hyperinsulinemia and hyperglycemia ( Tsao et al , 1996 ; Beckerman et al , 2021 ; Diaz-Vegas et al , 2024a ; Nelson et al , 2022 ). At the molecular level, muscle IR has been associated with excessive lipid accumulation within the tissue, a process known as lipotoxicity. This results in an increase in specific lipid intermediates, such as diacylglycerols (DAGs) and the sphingolipid ceramide, which are proposed to decrease insulin-induced GLUT4 translocation to the cell surface ( Perreault et al , 2018 ; Erion & Shulman, 2010 ; Turpin-Nolan et al , 2019 ). However, the relative importance of these lipids for IR is still debated ( Erion & Shulman, 2010 ; Chaurasia & Summers, 2021 ; Zierath, 2007 ). The cause of DAG accumulation in skeletal muscle IR remains unclear, but it is thought to result from an imbalance between fatty acid uptake and oxidation ( Koves et al , 2008 ). Excess fatty acid supply overwhelms mitochondrial oxidative capacity, diverting fatty acids towards de novo DAG synthesis, leading to cellular accumulation and contributing to IR ( Morino et al , 2006 ; Kraegen et al , 2009 ). Interestingly, in vivo studies using labelled tracers suggest that de novo DAG synthesis alone does not fully account for this accumulation ( Perreault et al , 2018 ), pointing to other pathways, such as phospholipid degradation, changes in the interconversion between sphingomyelin/ceramide, and direct delivery of DAGs by plasma lipoproteins ( Perreault et al , 2018 ). Plasma lipoproteins, especially chylomicrons and very low-density lipoproteins (VLDL), can transport various lipid species, including DAGs, in their core or on their surface ( Cohen & Fisher, 2013 ). These lipoproteins deliver a significant portion of their lipid content to muscle tissue, potentially contributing to a substantial amount of DAG accumulation, particularly in the context of obesity and IR ( Kim et al , 2001 ). Furthermore, adipocyte infiltration in skeletal muscle has been observed across various models of obesity ( Jani et al , 2021 ; Hilton et al , 2008 ; Wang et al , 2024 ), potentially altering muscle lipid composition and contributing to IR. Ceramide accumulation in skeletal muscle has also been strongly linked to IR ( Chaurasia & Summers, 2021 ). The primary mechanism driving ceramide accumulation in muscle is the de novo synthesis pathway, which begins with the condensation of serine and palmitoyl-CoA by the enzyme serine palmitoyltransferase - the rate-limiting step in this process ( Larsen & Tennagels, 2014 ). Additional pathways increasing muscle ceramides include sphingomyelin hydrolysis and the salvage pathway, which are cell-autonomous processes ( Chaurasia & Summers, 2021 ). These pathways are also linked to muscle IR. For example, the salvage pathway for ceramide production is activated by chronic inflammation, a condition commonly seen in individuals with obesity ( Green et al , 2021 ). Furthermore, selective activation of sphingomyelin hydrolysis is sufficient to drive IR in rat myotubes ( Diaz-Vegas et al , 2023 ). Recent advancements in mass spectrometry-based global lipidomics have enhanced our understanding of muscle lipotoxicity. These analyses have highlighted the complexity of lipid species, their subcellular distribution and established correlative links between muscle lipid composition and the development of IR in mice and humans ( Turpin-Nolan et al , 2019 ; Perreault et al , 2018 ). While these studies highlight links between lipid species and IR, fully elucidating this relationship in the context of obesity remains challenging, particularly due to the genetic variance influencing IR development ( James et al , 2021 ; Hunter, 2005 ; Nelson et al , 2022 ). Recent studies using genetically diverse mouse populations have significantly advanced our understanding of metabolic diseases ( Masson et al , 2023 ; Chella Krishnan et al , 2023 ; Churchill et al , 2012 ; Nelson et al , 2022 ; van Gerwen et al , 2024 ). These models provide controlled environments, access to various biological tissues, and a broad range of phenotypic diversity, making them highly valuable for investigating complex diseases ( Masson et al , 2024 ). A prominent example is the Diversity Outbred Australia (DOz) mouse population, which exhibits remarkable phenotypic variation even in younger animals fed a healthy chow diet ( Masson et al , 2023 ). Here, we utilised the DOz mouse population to investigate the link between lipotoxicity in skeletal muscle and systemic IR across natural phenotypic variation by combining metabolic phenotyping with paired lipidomics, proteomics and transcriptomics. While DAGs correlated most strongly with IR, this relationship was dependent on adipose tissue mass, suggesting they may not directly regulate muscle insulin sensitivity. In support of this, DAGs were closely linked to leptin gene expression in skeletal muscle, an adipocyte specific transcript, indicating adiposity and possibly adipocyte infiltration as a major contributor to DAG accumulation in skeletal muscle. In contrast, very long-chain ceramides correlated with systemic IR independently of obesity and were strongly associated with molecular markers of mitochondrial stress, highlighting their potential role in skeletal muscle metabolic dysfunction. Methods Animal experiments Experiments were performed in accordance with NHMRC guidelines and under approval of the University of Sydney Animal Ethics Committee, approval numbers #1274 and #1988. All mice were maintained at 23°C on a 12 hr light/dark cycle. Animal experiments Male DOz mice were bred and housed at the Charles Perkins Centre, University of Sydney, NSW, Australia, as detailed in ( Masson et al , 2023 ). Animals were 10-12 wks of age at the start of experiments and had free access to a standard laboratory chow diet containing 16% calories from fat, 61% calories from carbohydrates, and 23% calories from protein (Specialty Feeds) or an in-house made high-fat diet (HFD) containing 60% calories from fat, 20% calories from carbohydrate, and 20% calories from protein (25% g casein, 0.4% g L-cystein, 16% g cornstarch, 9.3% g sucrose, 6.4% g cellulose, 31.3% g lard, 3.2% g safflower oil, 6.4% g AIN-93 mineral mix [MP Biomedicals], 1.3% g AIN-93 vitamin mix [MP Biomedicals], 0.3% g choline. Total adipose tissue and lean mass were measured with EchoMRI-900 (EchoMRI Corporation Pte Ltd, Singapore) at 14 wks of age (for chow) and at 8 or 24 wks (for HFD diet)). Glucose tolerance was determined by a glucose tolerance test at the same age by fasting mice for 6 h (from 07am) before oral gavage of 20% glucose solution in water at 2 mg/kg lean mass. Blood glucose concentration was measured by glucometer (Accu-Chek, Roche Diabetes Care, NSW, Australia) from tail blood 0, 15, 30, 45, 60, and 90 min after oral gavage of glucose. Blood insulin levels at the 0 and 15 min timepoints were measured by mouse insulin ELISA Crystal Chem USA (Elk Grove Village, IL) according to the manufacturer’s instructions. Homeostatic Model Assessment for IR (HOMA-IR) was calculated as follows: ‘Insulin (ng/mL)’ / (5803*10 6 )*10 12 /6 x ‘Blood glucose (mmol/L)’*18 / 405 Mice were sedated by pentobarbital (⅕ dilution) and quadriceps muscle removed and snap frozen in liquid nitrogen following cervical dislocation. Quadriceps muscle lipidomics Lipid extraction Lipid extraction was performed as previously reported ( Turner et al , 2018 ). Briefly, a two-phase lipid extraction from frozen quadriceps muscle (∼20 mg) was performed using the methyl-tert-butyl ether (MTBE)/methanol/water method. Frozen tissue was powdered in liquid nitrogen, transferred to 2 mL Eppendorf tubes, and homogenised in 0.2 mL methanol containing 0.01% butylated hydroxytoluene (BHT) using a sonicator water bath (Thermoline, model 505) in a cold room. The homogenates were spiked with an internal standard mixture consisting of 5 nmoles 19:0/19:0 phosphatidylcholine (PC) and d7-cholesterol, 2 nmoles 18:1/15:0 d7-diacylglycerol, d18:1/17:0 glucosylceramide, 17:17:0 phosphatidylethanolamine (PE), and d18:1/17:0 SM, 4 nmoles 14:0/14:0/14:0/14:0 cardiolipin, 500 pmoles d18:1/17:0 ceramide, and 200 pmoles d17:1 sphingosine and d17:1 S1P. Next, 1.7 mL MTBE was added, and the samples were sonicated for 30 minutes in an ice-cold water bath (Thermoline Scientific, Australia). Phase separation was induced by adding 417 µL of mass spectrometry-grade water, followed by vortexing at maximum speed for 30 seconds and centrifugation at 1000 x g for 10 minutes. The upper organic phase (∼600 µL) was transferred into a 96-well deep plate. The aqueous phase was re-extracted three times using the same MTBE/methanol/water mixture, and the organic phases were combined in the deep-well plate (∼1800 µL). The organic phase was then dried under vacuum using a Savant SC210 SpeedVac (Thermo Scientific). The dried lipids were resuspended in 500 µL of 80% methanol/0.2% formic acid/2mM ammonium formate, mixed at 2000 rpm for 5 minutes, and stored at −20°C until further analysis. Lipid detection Lipids were quantified using selected reaction monitoring (SRM) on a TSQ Altis triple quadrupole mass spectrometer, coupled with a Vanquish HPLC system (ThermoFisher Scientific). Lipid separation was achieved on a 2.1 x 100 mm Waters Acquity UPLC C18 column (1.7 µm pore size) with a flow rate of 0.28 mL/min. The mobile phase consisted of two components: phase A (0.1% formic acid and 10 mM ammonium formate in 60% acetonitrile/40% water) and phase B (0.1% formic acid and 10 mM ammonium formate in 90% isopropanol/10% acetonitrile). The total run time was 25 minutes, starting with 20% phase B for the first 3 minutes, then gradually increasing to 100% phase B from 3 to 14 min. The system was held at 100% phase B from 14 to 20 min, returned to 20% phase B by 20.5 min, and held there for an additional 4.5 min. Ceramides, sphingomyelin, sphingosine, and sphingosine-1-phosphate were identified using the [M+H]+ precursor ion, with product ions at m/z 262.3 (sphinganine), 264.3 (sphingosine), 266.3 (sphinganine), or 184.1 (sphingomyelin). Diacylglycerols were identified by the [M+NH4]+ precursor ion, with a product ion corresponding to the neutral loss of a fatty acid +NH3. Cardiolipins were identified by the [M+H]+ precursor ion and the product ion corresponding to the neutral loss of a diacylglycerol. Cholesterol was detected using a precursor ion at m/z 369.4 and a product ion at m/z 161.1. We measured glycerolipids (diacylglycerols; DAGs), glycerophospholipids (phosphatidylethanolamines; PEs, phosphatidylcholines; PCs, cardiolipin; CLs, and plasmalogen phosphatidylethanolamines; PEPs), cholesterol and sphingolipids (sphingosines; Sph, sphingosine-1-phosphates; S1Ps, sphingomyelins; SMs, Ceramides; Cers, and Hexosylceramides; Hexs) in quadriceps from all mice (Supplemental table 1). Lipid quantification Lipids were quantified by calculating the area under the peak for a specific precursor–product ion pair. Each peak contained at least 8–10 scans for that ion pair. Peak detection and integration was performed using TraceFinder. To determine the amount of each lipid, the ratio to its class-specific internal standard was multiplied by the amount of internal standard added, then divided by the mass of extracted tissue. A detailed description of these analyses can be found in ( Diaz-Vegas et al , 2024b ). Transcriptomics We randomly selected 92 chow fed animals for transcriptomic analysis. RNA from mouse quadriceps was extracted using a combined TRIzol/chloroform/RNeasy kit (Invitrogen/Sigma Aldrich/Qiagen) spin column protocol. Muscles were pulverised in liquid nitrogen and lysed in 0.5 mL Trizol with a Mixer Mill (MM400, Retsch) at room temperature and incubated for 5 min. 0.1 mL chloroform was added, tubes inverted ∼10 times and left at room temperature for 3 min, then spun at 14.000 g at 4C for 10 min. The supernatant was transferred to new tubes containing 0.3 mL 70% ethanol. The sample:ethanol mix was added to the RNeasy spin columns and RNA was extracted following the manufacturer’s instructions. RNA integrity was assessed by 4200 TapeStation System (Agilent). All samples had RNA Integrity Number equivalent greater than 8. KAPA mRNA HyperPrep Kit (Roche) was used for RNA sequencing library preparation following manufacturer’s instructions with 500 ng of RNA as input and sequenced on NovaSeq 6000 (Illumina). Quality of sequencing data was assessed with multiQC ( Ewels et al , 2016 ). Gene expression levels were quantified with Kallisto ( Bray et al , 2016 ) version 0.50.1 with the following flags: kallisto quant-b 100--single-l 50-s 20. Mouse index file was downloaded from (November 2023): https://github.com/pachterlab/kallisto-transcriptome-indices/releases . Transcript levels were estimated with the r package tximport ( Soneson et al , 2015 ) and EdgeR ( Robinson et al , 2010 ) was used to filter lowly expressed genes. No samples were excluded. Raw sequencing data have been deposited at Gene Expression Omnibus (accession number GSE290031 - reviewer token: qfybwmkwnlwjzsb). Proteomics Proteomics data has been published previously ( Masson et al , 2023 ) and has been deposited on ProteomeXchange (PXD042277). The data consisted of two fractions for each quadriceps muscle; a mitochondria enriched fraction and the non-mitochondrial fraction from 228 chow fed animals. The overlap with the lipidomics data was 197 animals. Proteomics raw data files were researched using DIA-NN (v1.8) using a library-free FASTA search against the reviewed UniProt mouse proteome (downloaded March 2023). The protease was set to Trypsin/P with one missed cleavage, N-term M excision, carbamidomethylation, and M oxidation options on. Peptide length was set to 7–30, precursor range 350–1650, and fragment range 300– 2000, and false discovery rate (FDR) set to 1%. Both the mitochondrial enriched and non-mitochondrial samples were filtered based on the mitocarta 3.0 and that a protein was quantified in at least 50% of samples. Protein intensities were log2 transformed and median normalized to account for technical variation in total protein levels across all samples. Data analysis All data analysis and visualisation were performed in R (version 4.1 - 4.3). Pearson correlation was calculated with cor.test function in base R and bicor calculated with WGCNA package ( Langfelder & Horvath, 2008 ). To determine the association between a lipid feature and HOMA-IR, we used the following linear model: Lipid feature ∼ α + β1 HOMA IR x β2 diet + β3 TimeOnDiet + ε fitted with the glm function. The type II sum of squares was calculated using the ANOVA function of the R car package. To correct lipid features for adiposity, we built a linear model for lipid feature ∼ adipose tissue mass + ε with lm base R function and worked with the residuals (ε). In general, all reported p-values were corrected for multiple testing using the false-discovery rate correction (p.adjust(p-value, method = “fdr”)) with one exception (the HOMA-IR quartile analysis). For the segmentation of animals in HOMA-IR quartiles across adiposity bins, animals were divided into 10-percentage-points bins starting from 15% adiposity (± 5 percentage points) and ending at 40%. This cut-off was chosen to ensure we had sufficient mice for the quartile analysis. We compared lipid features between lowest (Q1) and highest (Q4) HOMA-IR quartiles across the whole adiposity span with increments of 1 percentage points by t-test on log(x+1) transformed lipidomics data following q-value estimation by the fdrtool function in fdrtool package ( Strimmer, 2008 ). To test for mean difference of lipid features between Q1 and Q4 we applied one-sample t-tests to all log2 fold-changes across all adiposity bins with the null hypothesis: log2 fold change = 0 to assess if the average lipid abundance in Q4 is different to Q1. P-values were adjusted with the Bonferroni method. Gene set enrichment analysis was performed with ClusterProfiler ( Wu et al , 2021 ) and ReactomePA ( Yu & He, 2016 ). Results Outbred mice show significant phenotypic diversity 399 male DOz mice were fed high-fat diet (HFD) or standard chow for varying durations ( Fig. 1A ): 1) standard chow (n = 203); 2) HFD for 8 wks (n = 80) or for 15 wks (n = 116). Overall, we observed significant phenotypic diversity in all metabolic traits ( Fig. 1B-E ). Adipose tissue mass ( Fig. 1B ) and fasting insulin ( Fig. 1C ) both varied ∼250-fold from lowest to highest, and the Homeostatic Model Assessment for IR (HOMA-IR), a readout of systemic insulin sensitivity, varied 450-fold ( Fig. 1D ). Both diet and duration of feeding impacted all metabolic traits with the longer term HFD group having a significantly higher increase in adipose tissue mass, fasting insulin and HOMA-IR compared with the short term HFD group, which again was increased compared to the chow group ( Fig 1B-D ). Fasting blood glucose was increased with HFD, but no difference was seen between the two HFD groups ( Fig. 1E ). Fasting insulin was the main contributor to HOMA-IR, explaining 92% of the variance in HOMA-IR ( Fig. 1F ). Overall, DOz mice capture a huge phenotypic range of metabolic parameters. Download figure Open in new tab Figure 1. Metabolic traits across 399 Diversity Outbred Australia (DOz) mice fed high-fat diet or chow. A) Male DOz mice were either fed chow (n = 203) or a high-fat diet for 8 (n = 80) or 15 weeks (n = 116). The chow fed group was aged-matched to the 8 weeks HFD group. B & C) Fat mass (B) and fasting blood insulin (C) across all mice. Boxplots show traits grouped by diet and scatter plots show each trait ordered by blood insulin (lowest to highest). Insert shows the relationship between fat mass and blood insulin. For both traits, chow < 8 weeks < 15 weeks significantly different. D) HOMA-IR across diet groups; chow < 8 weeks < 15 weeks significantly different. E) Fasting blood glucose across diet groups; chow group significantly different from HFD groups only. F) Relationship between fasting blood insulin and HOMA-IR. Targeted lipidomics reveals that muscle diacylglycerols are associated with systemic insulin sensitivity Because of the strong link between lipotoxicity and IR we employed a targeted lipidomic strategy to identify specific lipid species in quadriceps muscle that were associated with insulin sensitivity. Of the lipids measured by our targeted approach, phosphatidylcholines (PCs) (38%), cholesterol (29%) and phosphatidylethanolamines (PEs) (21%) made up 87% of the total lipids measured ( Fig. S1A ). As with the metabolic parameters, muscle lipids varied greatly across the whole cohort ( Fig. 2A ). Dimension reduction revealed good clustering of the lipidome with chow and the longer term HFD group forming two distinct clusters and the short term HFD group was intermediate ( Fig. 2B ). Correlation analysis demonstrated that adipose tissue mass had a strong influence on the muscle lipid profile, as 55% of identified lipids had a significant association with this parameter ( Fig. 2C ). Overall, muscle DAGs showed a strong tendency toward overrepresentation in associating with adipose tissue mass (9 out of 11 DAGs quantified, p = 0.081, Fisher’s exact test) indicating that DAG levels in skeletal muscle may simply depend on the state of obesity. Additionally, muscle DAGs were the only lipid class that were overrepresented in their association to fasting insulin (p = 5e-4), fasting blood glucose (p = 8e-4) and systemic insulin sensitivity (HOMA-IR, p = 9e-4) ( Fig. 2C ) with sphingomyelins (SMs) overrepresented in the association with fasting insulin only (p = 0.02). DAGs containing unsaturated fatty acids with few double bonds (18:1-18:1, 16:0-18:1 and 18:1-18:2) were positively associated with these traits, whereas highly polyunsaturated DAGs (16:0-22:6 and 18:0-22:6) were inversely associated. Of note, ceramides showed an inverse relationship to adipose tissue mass, as nine of the 14 ceramide species measured were negatively associated with adipose tissue mass. The only ceramide to associate with fasting insulin and HOMA-IR was the C18 ceramide d18:2-18.0 although the association was weak (r = 0.16, p = 7e-3). Download figure Open in new tab Figure 2. Target lipidomics in quadriceps from 399 DOz male mice. A) Relative (top) and absolute (bottom) abundance of lipid classes quantified by targeted lipidomics. Both are ordered by abundance of absolute total phosphatidylcholine abundance (lowest to highest). B) Dimension reduction by multidimensional scaling (MDS) of all lipid features. C) Bicor correlation coefficient of all lipid features to fat mass, fasting blood insulin, HOMA-IR, blood insulin at 15 min during glucose tolerance test and fasting blood glucose. Overall, these data show that there is considerable variance in the skeletal muscle lipidome between individual DOz mice fed either chow or HFD, and the predominant source of variation in lipid levels appears to stem from differences in adiposity. Ceramides are associated with systemic insulin resistance independently of adipose tissue mass Given that adiposity is strongly associated with IR and the strong link between adiposity and DAGs in muscle, this prompted us to investigate whether adipose tissue mass mediated the observed relationship between DAGs and systemic insulin sensitivity. To do this, we used linear modelling to examine associations between systemic insulin sensitivity, measured by HOMA-IR, and muscle lipids independently of other variables such as diet or adipose tissue mass. Consistent with the previous correlation analysis, the lipids most strongly associated with HOMA-IR after adjusting for diet were DAGs (18:1-18:1, 16:0-18:1, and 18:1-18:2), the C18 ceramide (d18:2-18:0), and a polyunsaturated DAG (18:1-20:4) ( Fig. 3A ). Furthermore, two SMs were significantly associated with HOMA-IR. Download figure Open in new tab Figure 3. Linear modelling showing relationship between HOMA-IR and each lipid feature. A) Main effect of HOMA-IR on individual lipid features from linear models accounting for diet and time of diet. B) Main effect of HOMA-IR on individual lipid features corrected for overall fat mass from linear models accounting for diet and time of diet. Size of data points are sized to the absolute change in HOMA-IR. Interestingly, lipids that were associated with increased whole-body insulin sensitivity (decreased HOMA-IR) included long-chain SMs such as d18:1-18:0, as well as DAG species like 18:0-22:6. Intriguingly, long-chain ceramides such as d18:0-16:0 and d18:0-18:0 were also positively linked to insulin sensitivity ( Fig 3 ). Since our primary focus was on identifying muscle lipids with associations to IR, and since insulin sensitivity and muscle lipids were influenced by adiposity, we constructed a model where each lipid feature was corrected for overall adipose tissue mass. This allowed us to analyse the muscle lipidome and its association with IR independently of adipose tissue mass. Strikingly, this revealed that very long-chain ceramides (d18:1-22:0, d18:1-20:0, d18:1-24:0, d18:0-22:0, d18:0-20:0, d18:1-24:1) were all associated with increased HOMA-IR ( Fig. 3B ). In addition to these ceramides, the very long-chain hexosylceramide (Hex) species, d18:1-24:1 and d18:1-24:0, both derived from very long-chain ceramides, were also associated with HOMA-IR. Finally, both PCs and PEs, which are the primary phospholipids in cellular membranes, were positively associated with insulin sensitivity. Stratified quartile-based assessment of muscle lipids by HOMA-IR across the full range of adiposity To further explore how muscle lipids relate to whole-body insulin sensitivity independently of adiposity, we stratified mice into adiposity bins of 10-percentage-point increments and categorized them into HOMA-IR quartiles within each bin ( Fig. 4A & B). HOMA-IR was significantly different in the most insulin sensitive quartile (Q1) and the most IR quartile (Q4) across all bins, creating groups with similar adiposity but distinct insulin sensitivity. This analysis emphasises the strength of using DOz mice as in individual inbred mouse strains like C57BL/6J mice whole-body IR is always associated with increased adiposity ( Nelson et al , 2022 ). Bin sizes ranged from 30 to 58 animals ( Fig. 4B ). We examined the lipids between Q1 and Q4 in each adiposity bin with a particular focus on those lipids that were most strongly associated with IR ( Fig. 3 ). Two ceramides were nominally elevated in all but one bin and significantly increased in 50% of the bins, while DAGs showed no consistent changes across the bins ( Fig. 4C ). This is consistent with the hypothesis that DAGs are associated with systemic IR as a consequence of increased adiposity, whereas ceramides seem more intrinsic to the muscle itself. Download figure Open in new tab Figure 4. Stratified quartile-based assessment of muscle lipids by HOMA-IR across adiposity bins. A) Relationship between adiposity and HOMA-IR. Two adiposity bin of 10 percentage points highlighted. Within each bin, 1st and 4th quartile (Q) of HOMA-IR are shown. B) HOMA-IR across all adiposity bins. Number above indicates the total number of animals (Q1 + Q4). Significant difference between Q1 and Q4 in all adiposity bins. C) Comparison of ceramides (Cers) and diacylglycerols (DAGs) abundance between Q1 and Q4 relative to Q1 across all adiposity bins. D) Overall average difference between Q1 and Q4 across all adiposity bins. Significance tested by one-sample t-test (null hypothesis: log2 fold-changes centers around 0 (log2FC = 0)). E) Selected pathways associated with very long-chain ceramides from both transcriptomic (n = 92) and proteomic (n = 197) data. F) The relationship between total DAG abundance and leptin gene expression in skeletal muscle. G) The relationship between very long-chain ceramides and leptin gene expression. Error bars are standard error over mean (SEM). To expand this type of analysis to all lipid features, we performed one-sample t-tests on the log2 fold-changes across all bins to determine if they differed from 0. Overall, this showed that 10/14 ceramides were significantly different between Q1 and Q4 groups (9 increased with systemic IR), whereas only 5/11 DAGs (3 increased with IR) were different ( Fig. 4D ). Consistent with previous analysis ( Fig. 3 ), the very long-chain ceramides (20, 22 and 24 carbons) showed the largest increase in the insulin resistant group (>50% increase). Furthermore, all Hex and Sph species quantified in our data and 7/11 SMs were also significantly increased. We do note, as mentioned above, that three DAGs were significantly increased in the insulin resistant groups across all adiposity bins, however the average increase was <20% and the profile of change across the adiposity bins was less consistent ( Fig. S2A ). Furthermore, C18 ceramides, the muscle ceramide class often associated with impaired metabolic state ( Hammerschmidt & Brüning, 2022 ), were unchanged across all bins. Together, this shows that very long-chain ceramides and sphingolipids in general were the lipids that were most significantly and consistently increased in quadriceps muscle from insulin resistant animals and this occurred independently of adiposity. Molecular signatures of selected muscle lipid features Ceramides have been strongly associated with several cellular pathways including inflammation, oxidative stress and mitochondrial dysfunction ( Chaurasia & Summers, 2021 ). To understand the molecular underpinnings of elevated ceramide levels in skeletal muscle, we quantified the quadriceps transcriptome (Supplemental table 2) and proteome (Supplemental table 3) from a subset of chow fed mice and correlated these to the total abundance of all very long-chain ceramides elevated in the IR state. From the transcriptome, we found four major cellular pathways that were associated with very long-chain ceramides. These were mitochondria related pathways (e.g. ‘TCA cycle and respiratory electron transport’ and ‘Complex I biogenesis’), many terms involved in increased mRNA translation (e.g. Eukaryotic Translation Initiation’), overall cell cycle control and protein degradation (e.g. ‘SCF(Skp2)-mediated Degradation of p27/p21’) and stress related pathways (e.g. ‘Cellular response to chemical stress’ and ‘hypoxia’). ( Fig. 4E and Supplemental table 4). While protein signatures associating with very long-chain ceramides reflected some of these trends, particularly increased translation, it also revealed notable differences. We saw a total absence of mitochondrial-related gene sets enriched with very long-chain ceramide abundance on a protein level ( Fig. 4E ). This was quite striking and suggested that increased mitochondrial gene expression is counterbalanced by elevated protein turnover, preventing an increase in mitochondrial protein abundance. This indicated some form of mitochondrial stress, where enhanced transcription did not translate to higher protein levels. Similarly, while transcriptomic data pointed to upregulation of stress-related pathways, including cell cycle regulation and cellular stress responses, these pathways were negatively associated at the protein level, suggesting a post-transcriptional suppression of their functional impact ( Fig. 4E ). These pathways were largely driven by proteasomal components, suggesting that while very long-chain ceramides were associated with a transcriptional response aimed at enhancing proteasomal function, this did not translate into increased proteasomal protein abundance. This discrepancy could indicate either a translational bottleneck or increased proteasomal turnover, ultimately limiting the accumulation of proteasomal subunits despite increased mRNA expression. We noted that leptin gene expression was quantified in all muscle samples. Given that leptin is an adipocyte-specific gene that strongly correlates with overall adipocyte abundance ( Zhang et al , 2002 ), we can use this as a marker of intermuscular adipocyte infiltration. We speculated that DAGs, but not other lipids quantified in our data, might correlate with Lep expression in muscle, since adipocytes typically have high concentrations of DAGs ( Pradas et al , 2018 ). Not surprisingly, the total abundance of DAGs showed the strongest correlation with Lep expression ( Fig. 4F ), while very long-chain ceramides exhibited no such relationship ( Fig. 4G ). This further points towards the notion that DAGs were associated with IR as a product of obesity itself and probably as a consequence of contaminating adipocytes in the skeletal muscle. Discussion Excessive lipid accumulation in skeletal muscle is a well-recognised cause of IR, with debate over whether DAGs or ceramides are the key intermediaries driving this pathology. We leveraged the natural genetic variation in DOz mice to examine how obesity shapes muscle lipid composition and systemic IR. This approach enabled comparisons across extreme IR states while controlling for obesity levels. We showed that both DAGs and ceramides were both linked to whole-body insulin sensitivity, but the association of DAGs was largely explained by the obesity state, while ceramides, particularly very long-chain ceramides, were elevated in the insulin resistant state independently of adiposity. These ceramide species were further linked with the activation of cellular stress response pathways and the regulation of mitochondrial proteostasis, both of which are closely linked to the pathophysiology of IR ( Petersen et al , 2024 ; Greyslak et al , 2023 ; Diaz-Vegas et al , 2023 ). These findings suggest that while both DAGs and ceramides influence whole-body insulin sensitivity, ceramides may play a pivotal role in driving muscle IR. Our observation that very long-chain ceramides (C20, C22, and C24) exhibited the greatest increase in the insulin-resistant group across any range of adiposity was surprising, as the long chain C18 ceramide have been implicated as the main ceramide in muscle linked to insulin resistance ( Tonks et al , 2016 ; Bergman et al , 2016 ; Perreault et al , 2018 ). Despite this, some of the corroborating evidence for this claim is not straight forward. Knock out of CerS1, the enzyme thought to regulate C18 ceramide biosynthesis in skeletal muscle ( Levy & Futerman, 2010 ), while leading to reduced muscle levels of C18 ceramide and protection from diet induced IR, appears to do so via an indirect mechanism involving FGF21 production ( Turpin-Nolan et al , 2019 ). In fact, C18 ceramide is unlikely to drive muscle IR, as CerS1 inhibition depletes C18 ceramide while maintaining elevated very long-chain ceramide and this fails to restore insulin sensitivity in high-fat-fed mice ( Turner et al , 2018 ). Regardless, other human studies have reported changes in very-long chain ceramides in insulin resistant people ( Adams et al , 2004 ; Amati et al , 2011 ; Coen et al , 2010 ). Furthermore, LDL-bound C24:0 ceramides are elevated in plasma from people with diabetes and delivering lipoprotein-C24:0 ceramide conjugate to mice or myotubes induces muscle IR ( Boon et al , 2013 ). C22 and C24 ceramides have also been implicated in inducing endoplasmic reticulum stress in muscle cells and tissue in a CerS2-dependent manner ( McNally et al , 2022 ). Altogether, these data suggest that very long-chain ceramides may play a significant and underappreciated role in insulin resistance. The above discussion poses the question as to what the relationship is between different ceramide species? Six ceramide synthases (CerS1-6) regulate de novo ceramide synthesis in mammals, producing a restrictive set of C14 to C36 ceramides ( Turner et al , 2018 ). Long chain ceramides (C16 and C18) are often credited as having deleterious metabolic impact, whereas very long-chain ceramides, in particular C24, do not ( Hammerschmidt & Brüning, 2022 ). These observations mainly stem from genetic mouse models, where either Cers1 , Cers2 or Cers5/6 have been deleted. One of the confounding effects of many of these studies is that the reduction of one specific ceramide species is often accompanied by an elevation in the level of others. For example, Cers1 null mice display depletion of C18 ceramide, but have elevated levels of C16- and C22-ceramides ( Zhao et al , 2011 ; Ginkel et al , 2012 ; Turpin-Nolan et al , 2019 ). Similarly, decreasing CerS2 expression, which synthesises very chain-long ceramides, in mouse liver decreases C24:0 and increases C16 ceramides, which is thought to drive hepatic carcinoma ( Bickert et al , 2018 ; Raichur et al , 2014 ). To complicate matters even more, ceramide levels with specific acyl chain lengths do not always align with CerS expression. For example, despite high CerS2 expression in the small intestine, C16- and C18-ceramides are the predominant species ( Schiffmann et al , 2013 ). This suggests that factors beyond CerS expression and activity influence the acyl chain composition of ceramides in a given tissue. Ceramide production through alternative biosynthetic pathways, such as sphingomyelin hydrolysis and the salvage pathway, may markedly contribute to ceramide levels in skeletal muscle. While elevated very long-chain ceramides were associated with a transcriptional response promoting cell cycle progression and stress adaptation, our proteomic data suggest increased protein turnover, possibly as a compensatory response to proteotoxic stress. Rather than simple translational repression, this pattern may reflect heightened protein degradation, ensuring the removal of damaged or misfolded proteins. In fact, ceramide accumulation has been linked with protein stability in both in vitro and in vivo studies ( Diaz-Vegas et al , 2023 ; Greyslak et al , 2023 ; Turner et al , 2018 ). This could be explained by the increase in mitochondrial oxidative stress observed with ceramide overload. For example, ceramides can directly inhibit respiratory complex III leading to production of reaction oxygen species ( Gudz et al , 1997 ), which in turn promotes mitochondrial proteostasis ( Guarás et al , 2016 ). This suggests that elevated ceramides impose cellular stress that disrupts normal protein homeostasis, requiring active protein degradation to counteract potential proteotoxic effects. Another question is how do ceramides drive IR? Mechanistically, ceramides can disrupt membrane fluidity and induce gel-to-fluid phase transitions and these properties are dependent on chain length and saturation status ( Pinto et al , 2011 ). This is particularly important in mitochondria, where respiratory complexes are highly sensitive to membrane fluidity, and excessive gel-to-fluid transitions have been associated with respiratory deficiencies ( Mileykovskaya & Dowhan, 2014 ). Furthermore, increased ceramides specifically in mitochondria are strongly linked with muscle IR in in vitro and in vivo studies ( Petersen et al , 2024 ; Perreault et al , 2018 ; Diaz-Vegas et al , 2023 ; Greyslak et al , 2023 ). We propose that accumulation of very long-chain ceramides within mitochondria induces proteostatic and oxidative stress, driving muscle IR. This is supported by recent evidence demonstrating that mitochondrial ceramide levels, but not DAGs, are strong predictors of muscle IR regardless of the obesity state in humans ( Petersen et al , 2024 ). However, determining how different lipid species impact mitochondrial membranes and respiratory complexes requires higher-resolution structural analyses ( Mileykovskaya & Dowhan, 2014 ) and detailed subcellular lipid profiling ( Perreault et al , 2018 ; Petersen et al , 2024 ). Finally, we showed that leptin gene expression in skeletal muscle was highly associated with DAGs. The most likely explanation is that the primary contribution to elevated DAGs in muscle that are associated with adiposity, is the presence of infiltrating adipocytes in the muscle. In fact, magnetic resonance imaging has demonstrated a high level of adipocyte infiltration within skeletal muscle tissue in obesity and type 2 diabetes ( Hilton et al , 2008 ). Similar observations have been made in mice fed a HFD ( Khan et al , 2015 ). These adipocytes may arise from resident fibro-adipogenic progenitors, which have the capacity to differentiate into adipocytes and contribute to increased intermuscular lipid accumulation in obesity ( Jia & Sowers, 2019 ). Regardless of their origin, adipocytes generally contain high concentrations of DAGs due to their primary role in lipid storage ( Pradas et al , 2018 ). Therefore, even a smaller number of infiltrating adipocytes could significantly alter the muscle DAG profile. In summary, our study links both DAGs and ceramides to whole-body IR, with DAGs driven by adiposity, and very long-chain ceramides potentially contributing as a cell-autonomous driver. While further research is needed to clarify the mechanisms behind this accumulation, our findings suggest that, beyond the current focus on C18 ceramides, targeting very long-chain ceramides could be a promising therapeutic strategy. Additionally, accounting for obesity will be crucial in future studies on muscle lipidome and whole-body metabolism. Author contributions: CRediT Conceptualisation: SM, DEJ. Methodology: SM, JS, DEJ, GM. Formal Analysis, SM, CDR, HBC. Data curation: SM. Investigation: SM, HBC, KCC, MP, JK, SWCM, AH, JC, ADV, DEJ. Writing – Original Draft: SM, ADV. Writing – Review & Editing, All authors. Visualisation, SM. Supervision, DEJ; Funding Acquisition, DEJ. Declaration of competing interest The authors declare no competing financial interests that could have impacted the work presented in this paper. Supplementary figure legends Download figure Open in new tab Figure S1. A) Absolute abundance of lipid classes quantified by targeted lipidomics ordered by abundance of absolute total phosphatidylcholine abundance. Estimates of total proportion by lipid class in percent are indicated to the right (relative mean of each class across 399 mice). Download figure Open in new tab Figure S2. A) Comparison of ceramides (Cers) and diacylglycerols (DAGs) abundance between Q1 and Q4 relative to Q1 across all adiposity bins. Acknowledgement This work was supported by National Health and Medical Research Council (NHMRC) Project Grants: GNT1120201 and GNT1061122 to D.E.J.; GNT2013621 to D.E.J. and A.D.V; D.E.J. is an Australian Research Council (ARC) Laureate Fellow. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NHMRC or ARC. 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Share Very long-chain ceramides in muscle associate with insulin resistance independent of obesity Søren Madsen , Harry B Cutler , Kristen C Cooke , Meg Potter , Jasmine Khor , Christoph D Rau , Stewart WC Masson , Anna Howell , Zora Modrusan , Anthony S Don , Jacqueline Stöckli , Alexis Diaz Vegas , David E James bioRxiv 2025.03.04.641442; doi: https://doi.org/10.1101/2025.03.04.641442 Share This Article: Copy Citation Tools Very long-chain ceramides in muscle associate with insulin resistance independent of obesity Søren Madsen , Harry B Cutler , Kristen C Cooke , Meg Potter , Jasmine Khor , Christoph D Rau , Stewart WC Masson , Anna Howell , Zora Modrusan , Anthony S Don , Jacqueline Stöckli , Alexis Diaz Vegas , David E James bioRxiv 2025.03.04.641442; doi: https://doi.org/10.1101/2025.03.04.641442 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Systems Biology Subject Areas All Articles Animal Behavior and Cognition (7637) Biochemistry (17705) Bioengineering (13899) Bioinformatics (41968) Biophysics (21460) Cancer Biology (18603) Cell Biology (25526) Clinical Trials (138) Developmental Biology (13385) Ecology (19910) Epidemiology (2067) Evolutionary Biology (24328) Genetics (15614) Genomics (22513) Immunology (17741) Microbiology (40423) Molecular Biology (17193) Neuroscience (88646) Paleontology (667) Pathology (2835) Pharmacology and Toxicology (4827) Physiology (7647) Plant Biology (15160) Scientific Communication and Education (2046) Synthetic Biology (4302) Systems Biology (9825) Zoology (2271)