Abstract
Skeletal muscle plays a central role in the regulation of systemic metabolism during lifespan. With aging, this function is perturbed, initiating multiple chronic diseases. Our knowledge of mechanisms responsible for this decline is limited. Glycerophosphocholine phosphodiesterase 1 (Gpcpd1) is a highly abundant muscle enzyme that hydrolyzes glycerophosphocholine (GPC). The physiological functions of Gpcpd1 remain largely unknown. Here we show, in mice, that the Gpcpd1–GPC metabolic pathway is perturbed in aged muscles. Further, muscle-specific, but not liver- or fat-specific, inactivation of Gpcpd1 resulted in severely impaired glucose metabolism. Western-type diets markedly worsened this condition. Mechanistically, Gpcpd1 muscle deficiency resulted in accumulation of GPC, causing an ‘aged-like’ transcriptomic signature and impaired insulin signaling in young Gpcpd1-deficient muscles. Finally, we report that the muscle GPC levels are markedly altered in both aged humans and patients with type 2 diabetes, displaying a high positive correlation between GPC levels and chronological age. Our findings reveal that the muscle GPCPD1–GPC metabolic pathway has an important role in the regulation of glucose homeostasis and that it is impaired during aging, which may contribute to glucose intolerance in aging.
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Data availability
The authors declare that the data supporting the findings of this study are available within the paper and its source data files, and are available from the corresponding authors upon request. Further information and requests for resources and reagents should be directed to and will be fulfilled by the lead contacts Josef M. Penninger (josef.penninger@ubc.ca) or Domagoj Cikes (domagoj.cikes@imba.oeaw.ac.at). The original datasets used in the RNA sequencing analysis can be accessed at National Center for Biotechnology Information archived under Gene Expression Omnibus accession codes GSE158850 and GSE40551 for human studies. For the mouse study, the quant-seq analysis can be obtained under the accession number GSE245285. For pan tissue analysis in Extended Data Fig. 2, GeneAtlas MOE430, probeset 1429144_at on GeneChip HT Mouse Genome MG-430A (Mus musculus) from the BioGPS portal (http://biogps.org/#goto=welcome) was used. Mass spectrometry lipidomics source data for Extended Data Fig. 6 is provided under a supplementary data file with the paper. Proteomics data can be accessed at PRIDE, with accession code PXD047373.
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Acknowledgements
We thank all members of our laboratories for helpful discussions. We are grateful to the Transgenic Unit, Comparative Medicine and Metabolomics Unit from Vienna Biocenter Core Facilities for their service. We also thank M.G. from the Histopathology unit in Vienna Biocenter Core Facilities for histological processing. We also thank the lipidomic service of Center for Molecular Medicine. We thank P. Krumpolec from the Biomedical Research Center, Institute of Experimental Endocrinology, Slovak Academy of Sciences, Bratislava, Slovakia, for the processing of MRS data from young and senior participants. We thank S. Trattnig, High Field MR Centre, Department of Biomedical Imaging and Image Guided Therapy for logistic support. We thank J. D. Johnson from the University of British Columbia, Vancouver, Canada for critical reading of the manuscript. J.M.P. is supported by the Institute of Molecular Biotechnology of Austrian Academy of Sciences, a Wittgenstein award, the T. von Zastrow foundation and a Canada 150 Research Chair in functional genetics. D.C. is supported by the Austrian Academy of Sciences and the T. von Zastrow foundation. M. Krssak, R.K. and L.P. are supported by Austrian Science Foundation (KLI-904-B). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.
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D.C. and J.M.P. conceived, coordinated and designed the study. D.C. and M.O. performed experiments and analyzed the data with contributions from M.L. and S.J.F.C. M.N. performed the bioinformatic analysis. A.H. assisted in tissue sampling. E. Ru. and T.G. collected human muscle biopsies. L.P., R.K. and M. Krssak performed and analyzed in vivo MRS measurements. B.L., M.L., C.B., C.K., G.T., V.B. and C.M. performed glucose uptake experiments. N.V. generated and analyzed mRNA levels in the clinical cohort. G.D. and E. Rö. performed mass spectrometry experiments. M.G. performed histological processing and stainings. M.L., M. Krebs, M. Krssak and A.K.-W. designed, coordinated and oversaw the human T2DM experiments. D.C. and J.M.P. wrote the manuscript. All authors edited the manuscript and approved the final manuscript.
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Extended data
Extended Data Fig. 1 Assessment of age-related Gpcpd1 levels in rat tissues.
Correlation of mRNA expression of Gpcpd1 in the indicated rat tissues with age. Data are shown as means ± SEM. Student’s two tailed, unpaired t-test was used for statistical analysis unless otherwise stated. There were no statistically significant differences.
Extended Data Fig. 2 Gpcpd1 mRNA expression across different cell and tissue type.
Gpcpd1 mRNA transcript per million in different tissues and cell types. Data represent mean Gpcpd1expression levels (z-scores) ± SD of from microarray analysis. A value bigger than 5 indicates expression in the corresponding tissue.
Extended Data Fig. 3 Validation of Gpcpd1 protein disruption in the skeletal muscle from MckCre-Gpcpd1flox/flox mice by mass spectrometry.
a) Scheme of the KO strategy upon Cre activation b) Representative extracted ion chromatograms (XICs) of Gpcpd1 peptides downstream of exon 8 of the Gpcpd1 locus from skeletal and heart muscle proteome of Control and MckCre-Gpcpd1flox/flox mice. Integrated signal used for quantification is indicated in grey. Peptide identifications are represented by red dashed lines. XICs from N = 3 mice per group are shown. c) RT-PCR based validation of Gpcpd1 deletion from various tissues in the Mck-CreGpcpd1flox/flox mice. N = 5 animals per each group. Data are shown as means ± SEM. Student’s two tailed, unpaired t-test was used for statistical analysis. ***p < 0.001, ****p < 0.0001.
Extended Data Fig. 4 Grip strength, muscle weights, fiber type, and muscle ultrastructure assessment of Mck-CreGpcpd1flox/flox mice.
a) Skeletal muscle weights (quadriceps, QA; gastrocnemicus, GC; and Tibialis anterior, TA) of 20 months old Control and Mck-CreGpcpd1flox/flox mice N = 6 per group, respectively. b) Representative images and quantification of MyHC!, MyhCIIA and MyHCIIB fibers in skeletal muscle (quadriceps) from 20 months old Control and Mck-CreGpcpd1flox/flox mice. Images were taken under 5x magnification, and ≥100 myofibers were counted at 3 different matching histological areas. N = 4 animals per group. Scale bar 500µm. c) Representative cross sections of skeletal muscle (quadriceps) and myofiber diameter size from 20 months old Control (N = 570 myofibers) and Mck-CreGpcpd1flox/flox mice (N = 640 myofibers). Myofibers were imaged using 10X magnification with ≥ 100 myofibers analyzed per mouse, isolated from 4 Control and 4 Mck-CreGpcpd1flox/flox mice. Scale bar 100µm. d) Ultrastructure of skeletal muscle (quadriceps) and e) intermyofibrillar mitochondria from 20 months old control and Mck-CreGpcpd1flox/flox mice. No apparent structural defects were observed in the muscles of Mck-CreGpcpd1flox/flox mice. n = 3 animals per group. Scale bar = 1µm and scale bar = 500nm respectively. f, g) Muscle strength evaluation of 20 months old Control (N = 6) and MckCre-Gpcpd1 flox/flox mice (N = 9). Unless otherwise stated, each dot represents one individual animal. Data are shown as means ± SEM. Student’s two tailed, unpaired t-test with Welch correction was used for statistical analysis unless otherwise stated, ns, not significant.
Extended Data Fig. 5 Fasting glucose levels and tissue glucose uptake in Mck-CreGpcpd1flox/flox mice.
a) Fasting blood glucose levels in 12 weeks old Control (N = 16) and Mck-CreGpcpd1flox/flox mice (N = 17). b) Blood glucose levels and c) Area under curve (AUC) after an oral glucose tolerance test (OGTT) in 20 month old months old Control and MckCre-Gpcpd1flox/flox mice. N = 6 per each group. Student’s two tailed un-paired t test with Welch correction was used for AUC statistical analysis. d) and e) Radioactive 2DG glucose uptake in heart muscle of 12 weeks old Control (N = 6) and Mck-CreGpcpd1flox/flox mice (N = 11) after bolus glucose feeding. f) Radioactive 2DG glucose uptake in white fat (eWAT), beige fat (iWAT), brown fat (BAT), and liver of 12 weeks old control (N = 6) and Mck-CreGpcpd1flox/flox mice (N = 11) after bolus glucose feeding. Each dot represents one individual animal. Data are shown as means ± SEM. Student’s two tailed, unpaired t-test with Welch correction was used for statistical analysis unless otherwise stated; ns, not significant; *p < 0.05, **p < 0.01, ***p < 0.001.
Extended Data Fig. 6 Muscle lipidome evaluation upon Gpcpd1 loss and C2C12 derived myotubes GPC treatment.
a) Mass spectrometry based un-targeted lipidomic analysis in quadriceps muscles isolated from 3 months old Control and Mck-Cre-Gpcpd1flox/flox littermate mice. Abbreviations denote triacylglycerols (TAG), sphingomyelins (SM), phosphatidylinositols (PI), phosphatydilethanolamines (PE), phosphatydilcholines (PC), lysophosphatydilethanolamines (LPE), lysophosphatydilcholines (LPC), diacylglycerols (DAG), ceramides (Cer), and cholesterolesther (CE). Numbers denote the carbon numbers, heatmap denotes higher and lower abundant lipid species in the muscles. No significant differences were found in the indicated lipid species abundance between muscles isolated from Control and Mck-Cre-Gpcpd1flox/flox mice. N = 5 per group. b) Choline levels in skeletal muscle (quadriceps) of 3 months old Control (N = 10) and MckCre-Gpcpd1flox/flox mice (N = 12). c) Correlation between tissue osmolarity and rising GPC levels in young and old mice. Each dot represents individual mice. d) GPC levels in C2C12 differentiated myotubes without (N = 5) or with GPC pretreatment (N = 5). Each dot represents one cell culture; error bars depict standard deviation. e) Cellular osmolarity of C2C12 derived myofibers treated with vehicle (N = 9) or GPC (N = 9) for 7 days ; error bars depict ; error bars depict standard deviation. f) Glucose uptake of C2C12 differentiated myotubes treated with vehicle or GPC for 7 days. Each dot represents one cell culture replicate (N = 6 per each condition); error bars depict Standard Deviation. Two-way ANOVA followed by Tukey’s multiple comparison test was used to analyze this data set. Data are shown as means ± SEM unless stated otherwise. Student’s two tailed, unpaired t-test was used for statistical analysis unless otherwise stated, ns, not significant, ****p < 0.0001.
Extended Data Fig. 7 Insulin signaling pathway s is affected in muscles from young Mck-CreGpcpd1flox/flox mice.
Analysis of insulin signaling network on transcriptional level in quadriceps from young 3 months old Control and MckCre-Gpcpd1flox/flox mice. The significantly downregulated genes (P < 0,05) are labelled in green.
Extended Data Fig. 8 Muscles from young Mck-CreGpcpd1flox/flox mice display an ‘aged-like’ transcriptomic signature.
Comparison of upregulated (red) and downregulated (blue) gene expression changes in aged skeletal muscles in rats (27 month old versus 6 month old) 16 with gene expression changes of skeletal muscles from 3 month old Control and Mck-CreGpcpd1flox/flox mice. Overlapping dysregulated genes are highlighted.
Extended Data Fig. 9 Correlation of GPC and GPC-PDE levels in humans with fat mass, muscle mass and body mass index.
Correlation of skeletal muscle Glycerophosphocholine (GPC) and Glycerophosphocholine phosphodiester (GPC-PDE) levels with fat mass, muscle mass and body mass index in humans, irrespective of age. No significant correlation was found for any of the parameters. Two-tailed Person correlation test was used for the analysis.
Supplementary information
Supplementary Data
Untargeted lipidome analysis of skeletal muscle at P10.
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Cikes, D., Leutner, M., Cronin, S.J.F. et al. Gpcpd1–GPC metabolic pathway is dysfunctional in aging and its deficiency severely perturbs glucose metabolism. Nat Aging 4, 80–94 (2024). https://doi.org/10.1038/s43587-023-00551-6
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DOI: https://doi.org/10.1038/s43587-023-00551-6
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