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Oxidative pentose phosphate pathway controls vascular mural cell coverage by regulating extracellular matrix composition

Nature Metabolism volume 4, pages 123–140 (2022)Cite this article

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Abstract

Vascular mural cells (vMCs) play an essential role in the development and maturation of the vasculature by promoting vessel stabilization through their interactions with endothelial cells. Whether endothelial metabolism influences mural cell recruitment and differentiation is unknown. Here, we show that the oxidative pentose phosphate pathway (oxPPP) in endothelial cells is required for establishing vMC coverage of the dorsal aorta during early vertebrate development in zebrafish and mice. We demonstrate that laminar shear stress and blood flow maintain oxPPP activity, which in turn, promotes elastin expression in blood vessels through production of ribose-5-phosphate. Elastin is both necessary and sufficient to drive vMC recruitment and maintenance when the oxPPP is active. In summary, our work demonstrates that endothelial cell metabolism regulates blood vessel maturation by controlling vascular matrix composition and vMC recruitment.

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Fig. 1: Oxidative pentose phosphate pathway regulates vascular maturation.
Fig. 2: Endothelial oxPPP controls vMC coverage of dorsal aorta in zebrafish.
Fig. 3: Endothelial Pgd drives vMC coverage of the DA in mouse embryos.
Fig. 4: oxPPP regulates ELASTIN expression in ECs.
Fig. 5: Hemodynamic forces promote vMC coverage through oxPPP and ELN expression.
Fig. 6: ELN rescues vMC recruitment in absence of functional oxPPP in ECs.
Fig. 7: oxPPP-dependent R5P levels are required for EC viability, ELN expression and vMC recruitment.
Fig. 8: Schematic representation of oxPPP function in ECs.

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Data availability

Expression data that support the findings of this study have been deposited in GEO with the accession code P0 with any restriction to access. In our article we also included public available gene expression data deposited in GEO with the accession code G2, made available others80. H. sapiens 38 (GRCh38) was used as reference genome. Source data are provided with this paper.

References

  1. Adams, R. H. & Alitalo, K. Molecular regulation of angiogenesis and lymphangiogenesis. Nat. Rev. Mol. Cell Biol. 8, 464–478 (2007).

    Article  PubMed  CAS  Google Scholar 

  2. Herbert, S. P. & Stainier, D. Y. Molecular control of endothelial cell behaviour during blood vessel morphogenesis. Nat. Rev. Mol. Cell Biol. 12, 551–564 (2011).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  3. Potente, M. & Makinen, T. Vascular heterogeneity and specialization in development and disease. Nat. Rev. Mol. Cell Biol. 18, 477–494 (2017).

    Article  PubMed  CAS  Google Scholar 

  4. Jain, R. K. Molecular regulation of vessel maturation. Nat. Med. 9, 685–693 (2003).

    Article  PubMed  CAS  Google Scholar 

  5. Bergers, G. & Song, S. The role of pericytes in blood-vessel formation and maintenance. Neuro. Oncol. 7, 452–464 (2005).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  6. Davis, G. E. & Senger, D. R. Endothelial extracellular matrix: biosynthesis, remodeling, and functions during vascular morphogenesis and neovessel stabilization. Circ. Res. 97, 1093–1107 (2005).

    Article  PubMed  CAS  Google Scholar 

  7. Mack, C. P. Signaling mechanisms that regulate smooth muscle cell differentiation. Arterioscler. Thromb. Vasc. Biol. 31, 1495–1505 (2011).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  8. Armulik, A., Genove, G. & Betsholtz, C. Pericytes: developmental, physiological, and pathological perspectives, problems, and promises. Dev. Cell 21, 193–215 (2011).

    Article  PubMed  CAS  Google Scholar 

  9. Ando, K. et al. Peri-arterial specification of vascular mural cells from naive mesenchyme requires Notch signaling. Development https://doi.org/10.1242/dev.165589 (2019).

  10. Li, X. & Carmeliet, P. Targeting angiogenic metabolism in disease. Science 359, 1335–1336 (2018).

    Article  PubMed  CAS  Google Scholar 

  11. Potente, M. & Carmeliet, P. The link between angiogenesis and endothelial metabolism. Annu. Rev. Physiol. 79, 43–66 (2017).

    Article  PubMed  CAS  Google Scholar 

  12. Stincone, A. et al. The return of metabolism: biochemistry and physiology of the pentose phosphate pathway. Biol. Rev. Camb. Philos. Soc. 90, 927–963 (2015).

    Article  PubMed  Google Scholar 

  13. Gaengel, K., Genove, G., Armulik, A. & Betsholtz, C. Endothelial-mural cell signaling in vascular development and angiogenesis. Arterioscler. Thromb. Vasc. Biol. 29, 630–638 (2009).

    Article  PubMed  CAS  Google Scholar 

  14. Santoro, M. M., Pesce, G. & Stainier, D. Y. Characterization of vascular mural cells during zebrafish development. Mech. Dev. 126, 638–649 (2009).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  15. Stratman, A. N. et al. Interactions between mural cells and endothelial cells stabilize the developing zebrafish dorsal aorta. Development 144, 115–127 (2017).

    PubMed  PubMed Central  CAS  Google Scholar 

  16. Whitesell, T. R. et al. foxc1 is required for embryonic head vascular smooth muscle differentiation in zebrafish. Dev. Biol. 453, 34–47 (2019).

    Article  PubMed  CAS  Google Scholar 

  17. Santoro, M. M. Zebrafish as a model to explore cell metabolism. Trends Endocrinol. Metab. 25, 546–554 (2014).

    Article  PubMed  CAS  Google Scholar 

  18. Schlegel, A. & Gut, P. Metabolic insights from zebrafish genetics, physiology, and chemical biology. Cell. Mol. Life Sci. 72, 2249–2260 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  19. Ando, K. et al. Clarification of mural cell coverage of vascular endothelial cells by live imaging of zebrafish. Development 143, 1328–1339 (2016).

    PubMed  PubMed Central  CAS  Google Scholar 

  20. Chen, X., Gays, D., Milia, C. & Santoro, M. M. Cilia control vascular mural cell recruitment in vertebrates. Cell Rep. 18, 1033–1047 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  21. Panieri, E., Millia, C. & Santoro, M. M. Real-time quantification of subcellular H2O2 and glutathione redox potential in living cardiovascular tissues. Free Radic. Biol. Med. 109, 189–200 (2017).

    Article  PubMed  CAS  Google Scholar 

  22. Lin, R. et al. 6-Phosphogluconate dehydrogenase links oxidative PPP, lipogenesis and tumour growth by inhibiting LKB1-AMPK signalling. Nat. Cell Biol. 17, 1484–1496 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  23. Hall, C. J. et al. Blocking fatty acid-fueled mROS production within macrophages alleviates acute gouty inflammation. J. Clin. Invest. 128, 1752–1771 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  24. Rumping, L. et al. GLS hyperactivity causes glutamate excess, infantile cataract and profound developmental delay. Hum. Mol. Genet. 28, 96–104 (2019).

    Article  PubMed  CAS  Google Scholar 

  25. Burmistrova, O. et al. Targeting PFKFB3 alleviates cerebral ischemia-reperfusion injury in mice. Sci. Rep. 9, 11670 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  26. Gays, D. et al. An exclusive cellular and molecular network governs intestinal smooth muscle cell differentiation in vertebrates. Development 144, 464–478 (2017).

    PubMed  CAS  Google Scholar 

  27. Ablain, J., Durand, E. M., Yang, S., Zhou, Y. & Zon, L. I. A CRISPR/Cas9 vector system for tissue-specific gene disruption in zebrafish. Dev. Cell 32, 756–764 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  28. Payne, S., De Val, S. & Neal, A. Endothelial-specific cre mouse models. Arterioscler. Thromb. Vasc. Biol. 38, 2550–2561 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  29. Ozerdem, U., Grako, K. A., Dahlin-Huppe, K., Monosov, E. & Stallcup, W. B. NG2 proteoglycan is expressed exclusively by mural cells during vascular morphogenesis. Dev. Dyn. 222, 218–227 (2001).

    Article  PubMed  CAS  Google Scholar 

  30. Etchegaray, J. P. & Mostoslavsky, R. Interplay between metabolism and epigenetics: a nuclear adaptation to environmental changes. Mol. Cell 62, 695–711 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  31. Janke, R., Dodson, A. E. & Rine, J. Metabolism and epigenetics. Annu. Rev. Cell Dev. Biol. 31, 473–496 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  32. Abdul-Wahed, A., Guilmeau, S. & Postic, C. Sweet sixteenth for ChREBP: established roles and future goals. Cell Metab. 26, 324–341 (2017).

    Article  PubMed  CAS  Google Scholar 

  33. Li, D. Y. et al. Elastin is an essential determinant of arterial morphogenesis. Nature 393, 276–280 (1998).

    Article  PubMed  CAS  Google Scholar 

  34. Wagenseil, J. E. & Mecham, R. P. Vascular extracellular matrix and arterial mechanics. Physiol. Rev. 89, 957–989 (2009).

    Article  PubMed  CAS  Google Scholar 

  35. Karnik, S. K. et al. A critical role for elastin signaling in vascular morphogenesis and disease. Development 130, 411–423 (2003).

    Article  PubMed  CAS  Google Scholar 

  36. Davis, E. C. Smooth muscle cell to elastic lamina connections in developing mouse aorta. Role in aortic medial organization. Lab. Invest. 68, 89–99 (1993).

    PubMed  CAS  Google Scholar 

  37. Wang, Y. et al. Ephrin-B2 controls VEGF-induced angiogenesis and lymphangiogenesis. Nature 465, 483–486 (2010).

    Article  PubMed  CAS  Google Scholar 

  38. le Noble, F. et al. Flow regulates arterial-venous differentiation in the chick embryo yolk sac. Development 131, 361–375 (2004).

    Article  PubMed  Google Scholar 

  39. Boselli, F., Freund, J. B. & Vermot, J. Blood flow mechanics in cardiovascular development. Cell. Mol. Life Sci. 72, 2545–2559 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  40. Ajami, N. E. et al. Systems biology analysis of longitudinal functional response of endothelial cells to shear stress. Proc. Natl Acad. Sci. USA 114, 10990–10995 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  41. Espinosa, M. G., Taber, L. A. & Wagenseil, J. E. Reduced embryonic blood flow impacts extracellular matrix deposition in the maturing aorta. Dev. Dyn. 247, 914–923 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  42. De Luca, E. et al. ZebraBeat: a flexible platform for the analysis of the cardiac rate in zebrafish embryos. Sci. Rep. 4, 4898 (2014).

    Article  PubMed Central  Google Scholar 

  43. Moriyama, Y. et al. Evolution of the fish heart by sub/neofunctionalization of an elastin gene. Nat. Commun. 7, 10397 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  44. Nordlund, P. & Reichard, P. Ribonucleotide reductases. Annu. Rev. Biochem. 75, 681–706 (2006).

    Article  PubMed  CAS  Google Scholar 

  45. Li, M. V. et al. Glucose-6-phosphate mediates activation of the carbohydrate responsive binding protein (ChREBP). Biochem. Biophys. Res. Commun. 395, 395–400 (2010).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  46. Stoltzman, C. A., Kaadige, M. R., Peterson, C. W. & Ayer, D. E. MondoA senses non-glucose sugars: regulation of thioredoxin-interacting protein (TXNIP) and the hexose transport curb. J. Biol. Chem. 286, 38027–38034 (2011).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  47. Mattila, J. et al. Mondo-mlx mediates organismal sugar sensing through the gli-similar transcription factor sugarbabe. Cell Rep. 13, 350–364 (2015).

    Article  PubMed  CAS  Google Scholar 

  48. Cairo, S., Merla, G., Urbinati, F., Ballabio, A. & Reymond, A. WBSCR14, a gene mapping to the Williams–Beuren syndrome deleted region, is a new member of the Mlx transcription factor network. Hum. Mol. Genet. 10, 617–627 (2001).

    Article  PubMed  CAS  Google Scholar 

  49. Kimmel, C. B., Ballard, W. W., Kimmel, S. R., Ullmann, B. & Schilling, T. F. Stages of embryonic development of the zebrafish. Dev. Dyn. 203, 253–310 (1995).

    Article  PubMed  CAS  Google Scholar 

  50. Braren, R. et al. Endothelial FAK is essential for vascular network stability, cell survival, and lamellipodial formation. J. Cell Biol. 172, 151–162 (2006).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  51. Claxton, S. et al. Efficient, inducible Cre‐recombinase activation in vascular endothelium. Genesis 46, 74–80 (2008).

    Article  PubMed  CAS  Google Scholar 

  52. Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J. 17, 10–12 (2011).

    Article  Google Scholar 

  53. Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).

    Article  CAS  PubMed  Google Scholar 

  54. Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  55. Risso, D., Schwartz, K., Sherlock, G. & Dudoit, S. GC-content normalization for RNA-seq data. BMC Bioinf. 12, 480 (2011).

    Article  CAS  Google Scholar 

  56. Robinson, M. D. & Smyth, G. K. Moderated statistical tests for assessing differences in tag abundance. Bioinformatics 23, 2881–2887 (2007).

    Article  PubMed  CAS  Google Scholar 

  57. Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. B Methodol. 57, 289–300 (1995).

    Google Scholar 

  58. Yu, G., Wang, L.-G., Han, Y. & He, Q.-Y. clusterProfiler: an R package for comparing biological themes among gene clusters. Omics 16, 284–287 (2012).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  59. Yu, G. & He, Q.-Y. ReactomePA: an R/Bioconductor package for reactome pathway analysis and visualization. Mol. Biosyst. 12, 477–479 (2016).

    Article  PubMed  CAS  Google Scholar 

  60. Bax, D. V., Rodgers, U. R., Bilek, M. M. & Weiss, A. S. Cell adhesion to tropoelastin is mediated via the C-terminal GRKRK motif and integrin αVβ3. J. Biol. Chem. 284, 28616–28623 (2009).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  61. Donninger, H. et al. Whole genome expression profiling of advance stage papillary serous ovarian cancer reveals activated pathways. Oncogene 23, 8065–8077 (2004).

    Article  PubMed  CAS  Google Scholar 

  62. Audano, M. et al. Zc3h10 regulates adipogenesis by controlling translation and F-actin/mitochondria interaction. J. Cell Biol. https://doi.org/10.1083/jcb.202003173 (2021).

  63. Magni, G. et al. Glial cell activation and altered metabolic profile in the spinal-trigeminal axis in a rat model of multiple sclerosis associated with the development of trigeminal sensitization. Brain Behav. Immun. 89, 268–280 (2020).

    Article  PubMed  CAS  Google Scholar 

  64. Palma, C. et al. Caloric restriction promotes immunometabolic reprogramming leading to protection from tuberculosis. Cell Metab. 33, 300–318 (2021).

    Article  PubMed  CAS  Google Scholar 

  65. M, A. et al. Mitochondrial dysfunction increases fatty acid β-oxidation and translates into impaired neuroblast maturation. FEBS Lett. https://doi.org/10.1002/1873-3468.13584 (2019).

  66. Ghaffari, M. H. et al. Metabolomics meets machine learning: longitudinal metabolite profiling in serum of normal versus overconditioned cows and pathway analysis. J. Dairy Sci. 102, 11561–11585 (2019).

    Article  PubMed  CAS  Google Scholar 

  67. Durbin, B. P., Hardin, J. S., Hawkins, D. M. & Rocke, D. M. A variance-stabilizing transformation for gene-expression microarray data. Bioinformatics 18, S105–S110 (2002).

    Article  PubMed  Google Scholar 

  68. van den Berg, R. A., Hoefsloot, H. C., Westerhuis, J. A., Smilde, A. K. & van der Werf, M. J. Centering, scaling, and transformations: improving the biological information content of metabolomics data. BMC Genomics 7, 142 (2006).

    Article  PubMed  PubMed Central  Google Scholar 

  69. Chong, J., Wishart, D. S. & Xia, J. Using MetaboAnalyst 4.0 for comprehensive and integrative metabolomics data analysis. Curr. Protoc. Bioinform. 68, e86 (2019).

    Article  Google Scholar 

  70. Heinrich, P. et al. Correcting for natural isotope abundance and tracer impurity in MS-, MS/MS- and high-resolution-multiple-tracer-data from stable isotope labeling experiments with IsoCorrectoR. Sci. Rep. 8, 17910 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  71. Midani, F. S., Wynn, M. L. & Schnell, S. The importance of accurately correcting for the natural abundance of stable isotopes. Anal. Biochem. 520, 27–43 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  72. Haffter, P. et al. The identification of genes with unique and essential functions in the development of the zebrafish, Danio rerio. Development 123, 1–36 (1996).

    Article  PubMed  CAS  Google Scholar 

  73. Kettleborough, R. N. et al. A systematic genome-wide analysis of zebrafish protein-coding gene function. Nature 496, 494–497 (2013).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  74. Gagnon, J. A. et al. Efficient mutagenesis by Cas9 protein-mediated oligonucleotide insertion and large-scale assessment of single-guide RNAs. PLoS ONE 9, e98186 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  75. Meeker, N. D., Hutchinson, S. A., Ho, L. & Trede, N. S. Method for isolation of PCR-ready genomic DNA from zebrafish tissues. Biotechniques https://doi.org/10.2144/000112619 (2007).

  76. Mugoni, V. et al. Ubiad1 is an antioxidant enzyme that regulates eNOS activity by CoQ10 synthesis. Cell 152, 504–518 (2013).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  77. Kim, H. J., Lee, H. J., Kim, H., Cho, S. W. & Kim, J. S. Targeted genome editing in human cells with zinc finger nucleases constructed via modular assembly. Genome Res. 19, 1279–1288 (2009).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  78. Facchinello, N., Schiavone, M., Vettori, A., Argenton, F. & Tiso, N. Monitoring Wnt signaling in zebrafish using fluorescent biosensors. Methods Mol. Biol. 1481, 81–94 (2016).

    Article  PubMed  CAS  Google Scholar 

  79. Covassin, L. et al. Global analysis of hematopoietic and vascular endothelial gene expression by tissue specific microarray profiling in zebrafish. Dev. Biol. 299, 551–562 (2006).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  80. Adami, M. et al. Simvastatin ointment, a new treatment for skin inflammatory conditions. J. Dermatol. Sci. 66, 127–135 (2012).

    Article  PubMed  CAS  Google Scholar 

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Acknowledgements

Research in the laboratory of M.M.S. is supported by the European Research Council Consolidator Grant-Rendox (ERC-CoG 647057) and AIRC (Associazione Italiana Ricerca sul Cancro) IG Grant 20119. N.F. was supported by a fellowship by Fondazione Umberto Veronesi. E.C. is supported by AIRC MFAG 23522. M.C. is supported by the Association Française contre la Myopathie (22134/6744359/I190311-2308) and by the Welcome Trust/Institutional Strategic Support Fund (199MCR/J22739). We thank D. Vigetti for reagents, S. Herkenne for neonatal retina angiogenesis experiments, F. Boldrin for TEM analyses, L. Pivotti and S. Iljazi for zebrafish handling, E. Marchesan for assistance in figure design, A. Cabrelle for technical support with FACS sorting and Y. Marc for immunostaining. We thank E. J. Corcoran for editorial and language assistance.

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Author notes

  1. These authors contributed equally: Nicola Facchinello, Matteo Astone.

Authors and Affiliations

  1. Laboratory of Angiogenesis and Cancer Metabolism, DiBio, University of Padua, Padua, Italy

    Nicola Facchinello, Matteo Astone, Roxana E. Oberkersch, Marianna Spizzotin & Massimo M. Santoro

  2. Department of Pharmacological and Biomolecular Sciences (DiSFeB), University of Milan, Milan, Italy

    Matteo Audano & Nico Mitro

  3. Department of Biology, University of Padua, Padua, Italy

    Enrica Calura

  4. Centre for Cardiovascular Science and Centre for Regenerative Medicine/Institute for Regeneration and Repair, University of Edinburgh, Edinburgh, UK

    Madalena Marques & Mihaela Crisan

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  1. Nicola Facchinello
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Contributions

M.M.S. conceived the concept of the study, provided supervision and wrote the manuscript. M.M.S. and N.F. were involved in the experimental design and drafting the manuscript. N.F. contributed to the execution, support and analysis of experiments and/or provided advice. R.O. performed AoSMC migration and adhesion as well as metabolic experiments. N.F. and M. Astone performed zebrafish and cell culture experiments. N.F., M. Astone and M.S. carried out mouse experiments and assays. M. Astone and N.F. performed LSS and retina angiogenesis experiments. M. Audano and N.M. performed the metabolomics analyses. E.C. performed bioinformatic analyses. M.C., M.M., N.F. and M.S. performed Pgd mouse embryo freezing, sectioning, immunostaining and analysis. All authors agreed on the final version of the manuscript.

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Correspondence to Massimo M. Santoro.

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Nature Metabolism thanks Yuqing Huo and the other, anonymous, reviewers for their contribution to the peer review of this work. Primary handling editors Christoph Schmitt, George Caputa

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Extended data

Extended Data Fig. 1 Generation of g6pd and pgd zebrafish mutants.

a, Schematic representation of the zebrafish g6pd gene. Exons are shown as green boxes. In g6pdsa24272 mutant an ENU-induced C>T nonsense mutation (red) in exon 6 of g6pd leads to a stop codon TAG (* shown in red). In the g6pd gene, the CRISPR targeted sequence is underlined and the protospacer adjacent motif (PAM) sequence is labelled in bold. In g6pduto70 mutant the resulting CRISPR-induced rearrangement of 6bp deletion and 20bp insertion in exon 10 (in italic) resulted in a frameshift mutation that leads to premature stop codon at aa 330 (* shown in red). b, Schematic representation of the zebrafish pgd gene. Exons are shown as light blue boxes. In pgdsa24360 mutant an ENU-induced G>T nonsense mutation (red) in exon 6 of pgd gene leads to a TAA stop codon (*shown in red). In the pgd gene, the CRISPR targeted sequence is underlined and the protospacer adjacent motif (PAM) sequence is labelled in bold. A CRISPR-induced deletion (7 bp) in exon 6 of pgd resulted a frameshift that leads to stop codon at aa 230 (* shown in red). c, Representative sequence chromatograms corresponding to g6pdwt and g6pduto70 animals. The deleted sequence in the mutant is highlighted in blue in the g6pdwt chromatogram and the inserted sequence in the mutant is highlighted in blue in g6pduto70 chromatogram. d, Representative sequence chromatograms corresponding to pgdwt and pgduto71 animals. The sequence deleted in the mutant is highlighted in blue in the pgdwt chromatogram and the new stop codons in yellow in the pgduto71 chromatogram. e, Representative images of the PCR gel from genotyping using genomic DNA from tail fins of adults born from an incross between g6pd+/uto70- or pgd+/uto71 heterozygotes. PCR products around 123 bp corresponding to the expected insertion size can be seen in g6pduto70 (left) while 113 bp corresponding to the expected deletion size can be seen in pgduto71(right).

Source data

Extended Data Fig. 2 Characterization of g6pd and pgd zebrafish mutants.

a, Progeny obtained by breeding heterozygous embryos (g6pd+/sa24272 or pgd+/uto71) were grown. According to Mendel’s law the offspring from an intercross will be distributed among three genotypes (50% heterozygous, 25% homozygous wt and 25% homozygous mutant. At different time points from 7dpf to 3mpf survived adult were genotyped and survival of homozygous mutants (g6pdsa24272 and pgduto71) and homozygous wt (g6pdwt and pgdwt) were plotted. Wt, grey lines, n=48; g6pdsa24272, green line, n =28; pgduto71 blue line, n=26. Two independent experiments for each zebrafish lines. Data are shown as mean ± SEM. Survival rate was compared between mutants and wild-types and a two-way ANOVA followed by Dunnet’s multiple comparison test was applied for statistical significance. b, Quantification of DA diameter in g6pdsa24272 and pgdsa24360 mutants. DA diameter has been measured in c Data are shown as mean ± SEM. c, Confocal images of z-projection of the trunk region (somite 8-14) of Tg(fli-n:GFP)y7 zebrafish embryos at 4dpf in pgduto71 compared to control (pgdwt). Scatter plots show the quantification of the number of EC in the DA in pgduto71 (n=3) compared to control (pgdwt) (n=6) from one independent experiments. Data are shown as mean ± SEM. Statistics were done using unpaired Student’s t-test. d, Quantification of the number of EC in the DA in g6pdsa24272 and pgdsa24360 mutants. The number of EC have been measured in Tg(kdrl:eGFP)s843 embryos, n≥7 from three independent experiments. Data are shown as mean ± SEM. e, Confocal images of partial z-projection of the trunk region (somite 8-14) of g6pdsa24272 zebrafish mutant in Tg(tagln:eGFP)uto37 (green) and TgBAC(pdgfrb:Gal4FF)ncv24;Tg(UAS:mcherry) (red) backgrounds at 4dpf. g6pdsa24272 mutants show diminished coverage of DA by vMC (arrows). Scale bar, 100μm. f, Scatter plots show the quantification of vMC coverage around the DA (dashed box area of panel g). Tg(tagln:eGFP)uto37;g6pdwt (n=24), Tg(tagln:eGFP)uto37;g6pdsa24272 (n=8), TgBAC(pdgfrb:Gal4FF)ncv24;g6pdwt (n=18), and TgBAC(pdgfrb:Gal4FF)ncv24;g6pdsa24272 (n=7) embryos from two independent experiments. Data are shown as mean ± SEM. Statistics were done using unpaired Student’s t-test. g, Log-2 fold changes of g6pd, pgd, cdh5, tie1, pdgfrb and acta2 genes obtained from Whitesell et all 16. g6pd and pgd are enriched in EC vs vMC. h, Strategy of FACS sorting of EC (GFP-positive) and vMC (mcherry-positive) from Tg(kdrl:eGFP)s843;Tg (acta2:mCherry)uto5 zebrafish embryos at 4dpf; eGFP vs mCherry plot show gates used for cell collection. Proportion of cells is equivalent to percentage of total sorted cells in each population. The acta2:mcherry+ and kdrl:EGFP+ cell population was 1-2% and 4-5% of the total cells recovered. i, FACS sorting of EC (eGFP-positive) and vMC (mCherry-positive) from Tg(kdrl:eGFP)s843;Tg (acta2:mCherry)uto5 zebrafish embryos at 4dpf followed by qRT-PCR analyses of g6pd and pgd (left), cdh5 and tie1 (center), and pdgfrb and acta2 (right). Mean expression in eGFP-positive cells was set to 100%, and mCherry positive cells are compared to this value. n≥3 biological independent samples (50 eGFP-positive and mCherry-positive embryos from Tg(kdrl:eGFP)s843/Tg(acta2:mCherry)uto5 were used in each sample) from two independent experiments. Data are shown as mean ± SEM. Statistics were done using unpaired Student’s t-test.

Source data

Extended Data Fig. 3 Generation and functional characterization of EC-specific g6pd and pgd zebrafish mutants.

a, Generation of CRISPR vectors for EC-specific gene targeting. Schematic representation of the two gRNA target sequences (gRNA1 and gRNA2) used to generate endothelial-specific KO for g6pd and pgd. gRNA sequence 1 for both genes is the one used to generate zebrafish mutants. The CRISPR targeted sequence is underlined. The red letters indicate the PAM region for each gene. b, Schematic representation of the workflow for EC-specific CRISPR-based KO technology. This illustration is a modification of BioRender templates. c, Schematic of FACS sorting from from Tg(kdrl:mCherry)uto2 zebrafish embryos to isolate EC (mCherry-positive). Relative mCherry mRNA expression indicated as arbitrary units (A.U.) from EC (mCherry-positive) and non-EC (mCherry-negative). n=3 from two independent experiments for each condition. Data are shown as mean ± SEM. Statistics were done using unpaired Student’s t-test. d, T7E1 assay for checking genomic mutations induced by g6pd or pgd injected plasmids. As previously described, cmlc2-eGFP negative and positive embryos were selected and genomic DNA was extracted from FACS-sorted zebrafish embryos at 3dpf and subjected for T7E1. Arrows indicate the expected positions of DNA bands cleaved by mismatch-sensitive T7E1. e, Quantification of DA diameter in g6pd and pgd EC-specific KO mutants. DA diameter has been measured in Tg(kdrl:eGFP)s843 embryos, n≥8 from 3 independent experiments. Statistics were done using unpaired Student’s t-test. Data are shown as mean ± SEM. f Confocal images of z-projection of the trunk region (somite 8-14) of Tg(fli-n:GFP)y7 zebrafish embryos at 4dpf after the injection with the Tol2 mRNA plasmid U6:pgdgRNA1; fli1a:Cas9 compared to control plasmid-injected embryos. Scatter plots show the quantification of the number of EC in the DA after the injection with the Tol2 mRNA plasmid U6:pgdgRNA1; fli1a:Cas9 (n=7) compared to control plasmid-injected embryos (n=6) from one independent experiments. Data are shown as mean ± SEM. g, Quantification of the number of EC in the DA in g6pd and pgd EC-specific KO mutants. The number of EC has been measured in Tg(kdrl:eGFP)s843 embryos, n≥6 from three independent experiments. Data are shown as mean ± SEM. Statistics were done using unpaired Student’s t-test.

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Extended Data Fig. 4 Generation of EC-specific Pgd KO mouse embryos.

a, Strategy to generate a conditional Pgd mutant allele in which exon 5 is flanked by lox sites (LoxP). The structures of the genomic locus, the targeting vector, and the targeted allele are shown. FRT-Neo-FRT, neomycin resistance cassette flanked by FRT sites. All Pgd KO lines were derived from the commercial EUCOMM line Pgd. In this line FRT-flanked lacZ-neo cassette is introduced before the floxed exon 5. Crossing with Flp1 mice deletes the lacZ-neo cassette and restores the gene function. The offspring can be bred to appropriate Cre-line to obtain a desired conditional KO line Pgd. b, Strategy for genotyping Pgd conditional allele (Pgdf/f). PCR of genomic DNA from tail of Pgdf/f (lane 1), control Pgd+/+(lane 2) and Pgdf/+ (lane 3) mice. The Pgd allele was detected by PCR using Pgd-F1 and Pgd-F2 primers. The WT locus yields an amplicon of 879 bp, whereas PCR of the floxed allele yields an amplicon of 976 bp. c, Strategy for monitoring Cre-mediated recombination in the EC-specific Pgd conditional allele (Pgd𝝙EC). PCR of genomic DNA from P5 Pgd𝝙EC (Tie2:Cre;Pgdf/f – lane 1), control (Pgdf/f – lane 2) and wt embryos. Recombination of the floxed Pgd allele (∆) occurs only in animals that are Tie2:Cre-positive and was detected by PCR using Pgd-F3 and Pgd-F4 primers. The primer pair generates a product of 893 bp only with the recombined pgd gene lacking the floxed region which includes exon 5 (Pgdf/f 1686 bp and wt 1543 bp). d, Representative bright-field images showing control and Pgd𝝙EC embryos at E9.5 and E10.5. No gross morphological defects are evident at these developmental stages among mutants and controls. Scale bars, 100μm e, Flow-cytometry gating strategy used to isolate EC/HC (CD31+ CD45-) and non-EC (CD31- CD45-). EC from embryo are typically ~1-3% of CD45- events. f, Relative Vegfr2 mRNA expression in arbitrary units (A.U.) were quantified by qRT-PCR in ECs and non-ECs isolated from embryos at E11. n=6 from three independent experiments. Data are shown as mean ± SEM. Statistics were done using unpaired Student’s t-test. g, Scatter plots show the percentage of FACS-sorted CD31+/CD45- cells from control (Pgdf/f) and Pgd𝝙EC mutants in single embryo at E11.0. n=6 (control) and n=5 (Pgd𝝙EC) biologically independent embryos, from three independent experiments. Data are shown as mean ± SEM. Statistics were done using unpaired Student’s t-test. h. Analysis of the efficiency of Cre-mediated recombination in ECs isolated by FACS from control and Pgd𝝙EC embryos at E11. Relative mRNA expression in arbitrary units (A.U.) of Pgd quantified by real time PCR in ECs (CD31+ CD45-). Ctrl (n=4) and Pgd𝝙EC (n=4) from two independent experiments were used. Data are shown as mean ± SEM. Statistical analysis was done using unpaired Student’s t-test.

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Extended Data Fig. 5 SMA+ and SM22+ mural cells are missing in PgdΔEC mouse embryos.

a, Sections of E11 control and Pgd𝝙EC mouse embryos immunostained with only secondary antibodies (negative controls) or primary antibodies against α-smooth muscle actin (αSMA) to detect vMC (red) and CD31 for ECs/HCs (green). DAPI (blue) stains all nuclei. D: dorsal, V:ventral, N: notochord. Scale bars: 50μm. b-c, DA sections of E11 control and Pgd𝝙EC mouse embryos immunostained with antibodies against α-smooth muscle actin (αSMA) or SM22 to detect vMC (red) and CD31 for EC/HC (green). DAPI (blue) stains all nuclei. Arrows and arrowheads indicate EC and vMC, respectively. D: dorsal, V: ventral, N: notochord. Scale bars, 20μm. d-e, DA sections of E11 control and Pgd𝝙EC mouse embryos immunostained with antibodies against α-smooth muscle actin (αSMA) (green) and SM22 (red) to detect vMC. DAPI (blue) stains all nuclei. Arrowheads indicate vMC. D: dorsal, V: ventral. Scale bars, 20μm (g) and 10μm (h).

Extended Data Fig. 6 Downregulation of endothelial G6PD and PGD in vivo and in vitro.

a-b, qRT-PCR expression analysis of G6PD and PGD in control, G6PDKD and PGDKD in HUVEC (n=4) (a) and HAEC (n=3) (b), respectively from three independent experiments were used. Data are shown as mean ± SEM. Statistics were done using one-way ANOVA followed by Tukey’s multiple comparison test. c, Western blot analysis of G6PD and PGD expression in control, G6PDKD and PGDKD HUVEC. VINCULIN (VCL) is used as loading control. d-e, Densitometric analysis of G6PD and PGD expression in HUVEC (d) and HAEC (e) relative to VINCULIN. In HUVEC (n=4 for Ab-G6PD and n=6 for Ab-PGD) and in HAEC (n=3) from three independent experiments. Data are shown as mean ± SEM. Statistics were done using one-way ANOVA followed by Tukey’s multiple comparison test. f, qRT-PCR expression analysis of LAMA4, FN1, COL4A1 and ELN in control, G6PDKD and PGDKD in HAEC. LAMa4, FN1, COL4A1 (n=3) and ELN (n=5) from three independent experiments were used. Data are shown as mean ± SEM. Statistics were done using one-way ANOVA followed by Tukey’s multiple comparison test. g, Confocal images of DA sections of control and Pgd𝝙EC mouse embryos at E11 that were immunosignal with anti-ELN (red) and anti-CD31 (green) antibodies. DAPI (blue) is used for nuclei staining. Scale bars, 50 μm. Images are representative of at least two independent experiments. h, Timeline for the analysis of postnatal mouse retina angiogenesis in Pgd KO mouse. Control and Pgdi𝝙EC mice were injected with tamoxifen at P2-P3-P4 and retinas analyzed at P6. i, qRT-PCR expression analysis of Pgd and Cdh5 in EC from mouse liver in control and Pgdi𝝙EC pups at P6. n=4 from three independent experiments. Data are shown as mean ± SEM. Statistics were done using unpaired Student’s t-test. j, Western Blot of PGD on MPEC isolated from control and Pgdi𝝙EC pups at P6. β-ACTIN is used as loading control. And CDH5 as EC marker. Images are representative of at least two independent experiments.

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Extended Data Fig. 7 ELN regulates adhesion and migration of MC.

a-c, Quantification of ECs area (a), vessel length (b), and branching points (c) per field at the angiogenic front of control and Pgdi𝝙EC pups at P6. Ctrl (n=13) and Pgdi𝝙EC (n=12) from six independent experiments. Data are shown as mean ± SEM. Statistics were done using unpaired Student’s t-test. d, Quantification of phopsho-histone3 (pHH3)-positive cells at the angiogenic front in control and Pgdi𝝙EC pups at P6. pHH3 is used as marker for proliferative EC. Ctrl (n=7) and Pgdi𝝙EC (n=6) from two independent experiments. Data are shown as mean ± SEM. Statistics were done using unpaired Student’s t-test. e, Representative images of transwell migration assay of AoSMCs treated with different concentrations of elastin (ELN) or 600uM R5P overnight. f, Quantification of migrating cells shown in panel e, using ImageJ. Data are shown as mean ± SEM of n=3 from two independent experiments. Statistics were assessed using one-way ANOVA followed by Bonferroni’s multiple comparison test. g, Cell adhesion assay of AoSMCs treated with elastin (ELN) or fibronectin (FN) for 40 min. The percentage of attached cells to total cell inputs was determined using crystal violet staining. Data are shown as mean ± SEM of n=3 from two independent experiments. Statistics were assessed using one-way ANOVA followed by Bonferroni’s multiple comparison test.

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Extended Data Fig. 8 LSS-dependent expression of G6PD, PGD and ELN in EC.

a, Effects of shear stress on the expression of G6PD and PGD genes exposed to 24h of LSS in human endothelial cell lines (HUVEC). KLF2 was used a positive control being a flow-induced gene. KFL2 (n=3), G6PD and PGD (n=6) from three independent experiments. Data are shown as mean ± SEM. Statistics were done using unpaired Student’s t-test. b, Shear stress increases G6PD and PGD expression in ECs. Laminar shear stress (LSS) induces oxPPP in pulsatile shear flow (PS) and in oscillatory shear flow (OS). Longitudinal transcriptional response of G6PD and PGD genes (10 time points, 42 RNAseq samples). Gene counts were obtained from fastq data from SRP117215 dataset of SRA 40 quantified using rsem and normalized with EDASeq. Data are shown as mean ± SEM. c, Effects of shear stress on the expression of different ECM target genes in human endothelial cell lines (HUVEC) after 24h of LSS. n=4 from three independent experiments. Data are shown as mean ± SEM. Statistics were done using unpaired Student’s t-test. d, (Top) Generation of CRISPR vectors for EC-specific gene targeting. Schematic representation, adapted from Ensembl, illustrating the exons in elna gene and the gRNA target sequence used to generate endothelial-specific KO for elna. The CRISPR targeted sequence is underlined. The red letters indicate the PAM region for each gene. (Bottom) Heteroduplex mobility assay of the PCR-amplified elna exon 5 fragment targeted by the CRISPR/Cas9 sgRNA. 1,2, and 3 represent three sgRNA-injected embryos; wt1 and wt2 are control embryos. e, Scatter plots showing the quantification of vMC coverage of Tg(kdrl:eGFP)s843 Tg(acta2:mCherry)uto5 embryos at 4dpf in g6pdwt and g6pdsa24272 with and without elna mRNA injected at 100ng/μl. g6pdwt (n=6), g6pdsa24272 (n=9), g6pdwt + elna 100ng/μl (n=6), g6pdsa24272 + elna 100ng/μl (n=10) embryos from two independent experiments. Data are shown as mean ± SEM. Statistics were done using one-way ANOVA followed by Tukey’s multiple comparison test.

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Extended Data Fig. 9 G6PD and PGD blockade affects oxPPP metabolites.

a, Gluconate-6P, ribulose/ribose/xylulose-5P, GAP/DHAP, glucose/fructose-6P, and sedoheptulose-7P relative abundance in control and G6PDKD cells. Control (n=5) and KD (n=4) from two independent experiments. Data are shown as mean ± SD. Statistics were assessed using Student’s t-test followed by false discovery rate (FDR) correction was applied. b, Relative NADP/NADPH ratio, ATP energy charge, and GSH/GSSG ratio in control and G6PDKD cells. Data are shown as mean ± SEM, parametric-unpaired Student’s t-test was applied. c, Total levels of nucleotides, ribonucleotides, and deoxyribonucleotides of control, G6PDKD and PGDKD EC. n=10 from two independent experiments. Data are shown as mean ± SEM. For statistical analysis the one-way ANOVA followed by Tukey’s test multiple comparison was applied.

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Extended Data Fig. 10 oxPPP blockade affects EC number.

a, Cell number of G6PDKD and PGDKD EC compared to controls. n=7 from two independent experiments. Data are shown as mean ± SEM. Statistics were done using one-way ANOVA followed by Dunnet’s multiple comparison test. b, Cell cycle analysis of G6PDKD and PGDKD EC by flow cytometry after propidium iodide staining. n=2 from two independent experiments. Data are shown as mean ± SEM. Statistics were done using two-way ANOVA followed by Tukey’s multiple comparison test. c, Western blot analysis of CYCLIN A, RRM2, G6PD and PGD expression in control, G6PDKD and PGDKD EC. VINCULIN (VCL) was used as loading control. The image is representative of at least two independent experiments. d, Confocal microscopy images of the DA region of control and Pgd𝝙EC embryos at E11 following staining for ERG (red) and CD31 (green). A=dorsal aorta, V= vein. Scale bars: 20 μm. Images are representative of at least two independent experiments. e, Confocal images of partial z-projection of the trunk region (somite 8-14) of a Tg(kdrl:eGFP)s843 ;Tg(acta2:mCherry)uto5 zebrafish embryos at 4 dpf in ctrl and in EC-specific g6pd mutants Tg(U6:g6pdgRNA1, fli1a:Cas9) with or without injection of R5P (500 mM). Scale bar, 100 μm. f, Scatter plots showing the quantification of vMC coverage around the DA in Tg(U6:g6pdgRNA1, fli1a:Cas9). ctrl (n=7), Tg(U6:g6pdgRNA1, fli1a:Cas9)(n=10), Tg(U6:g6pdgRNA1, fli1a:Cas9)+R5P (n=8) embryos from two independent experiments. Data are shown as mean ± SEM. Statistical analysis was done using one-way ANOVA followed by Tukey’s multiple comparison test.

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Supplementary information

Supplementary Information

Table of DEGs/Reactome pathway enrichment analyses/nucleotide sequences for shRNA, MO, sgRNA and primers for genotyping,

Reporting Summary

Supplementary Table

Mass spectrometry parameters for the detected metabolites both in negative and positive ion mode.

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Facchinello, N., Astone, M., Audano, M. et al. Oxidative pentose phosphate pathway controls vascular mural cell coverage by regulating extracellular matrix composition. Nat Metab 4, 123–140 (2022). https://doi.org/10.1038/s42255-021-00514-4

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