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Efficient megakaryopoiesis and platelet production require phospholipid remodeling and PUFA uptake through CD36

Nature Cardiovascular Research volume 2, pages 746–763 (2023)Cite this article

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Abstract

Lipids contribute to hematopoiesis and membrane properties and dynamics; however, little is known about the role of lipids in megakaryopoiesis. Here we show that megakaryocyte progenitors, megakaryocytes and platelets present a unique lipidome progressively enriched in polyunsaturated fatty acid (PUFA)-containing phospholipids. In vitro, inhibition of both exogenous fatty acid functionalization and uptake as well as de novo lipogenesis impaired megakaryocyte differentiation and proplatelet production. In vivo, mice on a high saturated fatty acid diet had significantly lower platelet counts, which was prevented by eating a PUFA-enriched diet. Fatty acid uptake was largely dependent on CD36, and its deletion in mice resulted in low platelets. Moreover, patients with a CD36 loss-of-function mutation exhibited thrombocytopenia and increased bleeding. Our results suggest that fatty acid uptake and regulation is essential for megakaryocyte maturation and platelet production and that changes in dietary fatty acids may be a viable target to modulate platelet counts.

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Fig. 1: Megakaryocytes and platelets have a unique lipid profile that is enriched in PUFAs.
Fig. 2: Fatty acid incorporation and de novo lipogenesis are necessary for MK differentiation and efficient proplatelet formation.
Fig. 3: A high-saturated-fat diet significantly alters MK phenotype and reduces platelet counts.
Fig. 4: Platelet counts can be modified in vivo by altering dietary PUFA composition.
Fig. 5: Lack of CD36 in mice reduces cellular fatty acid incorporation and impairs proplatelet formation.
Fig. 6: Megakaryocytes and platelet counts in Cd36−/− mice are not affected by high-fat diets enriched in fatty acids.
Fig. 7: Identification of a CD36 loss-of-function variant (p.Tyr325Ter) in patients with thrombocytopenia.

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Data availability

All data supporting the findings in this study are included in the main article and associated files. Source data are provided with this manuscript. All raw lipidomic data can be found at https://www.ebi.ac.uk/metabolights/MTBLS8042.

Code availability

Instructions and code for the automated pipeline analysis of proplatelet production from megakaryocytes can be found at https://github.com/broadinstitute/Italiano-MK-Analysis.

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Acknowledgements

This work is supported by the National Institute of Diabetes and Digestive and Kidney Diseases (R03DK124746 to K.R.M., R01DK112778 to J.L.) and the National Heart, Lung, and Blood Institute (R01HL151494 to K.R.M., 5T32HL007734 to T.I.H. and R35HL161175 to J.E.I.) (National Institutes of Health); fellowships from the American Society of Hematology (ASH Restart Award to M.N.B.) and the American Heart Association (23POST1011433 to M.N.B.); a Walter Benjamin Fellowship from the German Research Foundation (BE 7766/2-1 to I.C.B.); the Boston Children’s Hospital Surgical Foundation (T.I.H. and M.P.); the Wellcome Trust (218649/Z/19/Z to A.O.K.); the Saudi Arabia Cultural Bureau in London (I.A.); the National Health and Medical Research Council of Australia (APP1194329 to A.J.M. and Investigator Grant 2009965 to P.J.M.); the Fonds de Recherche en Santé du Québec and Canadian Institutes of Health Research (E.B.); and the US Department of Agriculture National Institute of Food and Agriculture (to J.L.).

We would like to thank C. Koo and M. Tomlinson from the University of Birmingham (Birmingham, UK) for assistance with in vitro characterization of the CD36 mutation and B. Nolan (Trinity College Dublin) for referring the patients to the GAPP study. We would also like to thank M. Laplante (University of Laval, Quebec, Canada) and his team for their help with experiments and for being an excellent collaborator. Thank you to J. Cardenas (The University of Texas Health Science Center at Houston) and L. Batista (Washington University in St. Louis) for their critical review of the manuscript, excellent edits and support during the publishing process.

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Authors and Affiliations

  1. Vascular Biology Program, Boston Children’s Hospital, Boston, MA, USA

    Maria N. Barrachina, Isabelle C. Becker, Thomas I. Hirsch, Daniela Freire, Karen Guo, Estelle Carminita, Joseph E. Italiano, Mark Puder & Kellie R. Machlus

  2. Harvard Medical School, Department of Surgery, Boston Children’s Hospital, Boston, MA, USA

    Maria N. Barrachina, Isabelle C. Becker, Thomas I. Hirsch, Estelle Carminita, Joseph E. Italiano, Mark Puder & Kellie R. Machlus

  3. Haematopoiesis and Leukocyte Biology, Baker Heart and Diabetes Institute, Melbourne, VIC, Australia

    Gerard Pernes, Pooranee K. Morgan, Thomas J. C. Collins & Andrew J. Murphy

  4. Centre de Recherche du CHU de Québec-Université Laval and Centre de Recherche ARThrite, Québec, QC, Canada

    Isabelle Allaeys & Eric Boilard

  5. Department of Pathobiology and Diagnostic Investigation, Michigan State University, East Lansing, MI, USA

    Dafna J. Groeneveld, Zimu Wei & James Luyendyk

  6. Institute of Cardiovascular Sciences, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK

    Abdullah O. Khan, Ibrahim Almazni & Neil V. Morgan

  7. MRC Weatherall Institute of Molecular Medicine, Radcliffe Department of Medicine and National Institute for Health Research (NIHR) Oxford Biomedical Research Centre, University of Oxford, Oxford, UK

    Abdullah O. Khan

  8. Metabolomics, Baker Heart and Diabetes Institute, Melbourne, VIC, Australia

    Natalie A. Mellett & Peter J. Meikle

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Contributions

M.N.B. conceived the study, performed experiments, collected, interpreted and analyzed data and wrote the manuscript. G.P., I.C.B. and I.A. performed experiments and collected and analyzed data. G.P. prepared all lipidomics samples. A.O.K. provided key intellectual input. E.C. designed and developed image analysis methodologies. D.J.G., D.F., K.G., Z.W. and I.A. performed experiments and analyzed data. T.I.H. and M.P. maintained the CD36 knockout mouse colony and provided experimental and intellectual input. N.V.M. recruited and governed patient ethics, performed experiments, analyzed data and helped write the manuscript. P.K.M. and T.J.C.C. analyzed lipidomic data and prepared corresponding figures. N.A.M. performed lipidomics experiments (with P.J.M.) and uploaded raw data to the metabolights server. P.J.M., J.L. and J.E.I. provided intellectual input and key reagents. E.B. provided reagents, intellectual input and interpreted data. A.J.M. helped conceive the lipidomics study, interpret the data and write the manuscript. K.R.M. conceived and directed the study, analyzed and interpreted data and wrote the manuscript. All authors provided input on and reviewed the manuscript.

Corresponding author

Correspondence to Kellie R. Machlus.

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Competing interests

J.E.I. has financial interest in and is a founder of Stellular Bio, a biotechnology company focused on making donor-independent platelet-like cells for regenerative medicine. The interests of J.E.I. are managed by Boston Children’s Hospital. All other authors have no conflicts of interest to declare that are relevant to the content of this article.

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Extended data

Extended Data Fig. 1 Megakaryocytes and platelets have unique lipidomic profiles.

Murine bone marrow cell populations were isolated by fluorescence-activated cell sorting and platelets by sequential centrifugation. Lipids were extracted and analyzed using 20-min gradient HPLC and mass spectrometry (see methods for details). (a) Percentage of different lipid classes of indicated murine bone marrow cell populations and autologous platelets in lipidomic analyses. Volcano plots from the different lipid classes between (b) bone marrow extracellular fluid (BMEF) and MEPs. n = 4 and n = 8, respectively, (c) BMEF and immature (CD41+) MKs, n = 4 (d) BMEF and mature (CD41/42+) MKs, n = 4, and (e) platelets and plasma n = 4. Total percentage of all acyl/alkyl composition of (f) PC (g) PI, (h) PE, (i) PS. n = 8 for MEP and 4 for all other cell types, biological replicates. All data are presented as mean +/− SD. MK: megakaryocyte; MEP: MK-erythroid progenitor; PA: phosphatidic acid; PC: phosphatidylcholine; PE: phosphatidylethanolamine; PI: phosphatidylinositol; PS: phosphatidylserine; PG: phosphatidylglycerol.

Source data

Extended Data Fig. 2 Fatty acid uptake and synthesis affected MK differentiation but not mitochondria metabolism.

(a) Fetal liver-derived HSPCs were cultured with TPO and treated with the ACSL inhibitor Triacsin C at indicated doses. CD41+ and CD41/CD42d+ cells were quantified using flow cytometry, n = 5, one-way ANOVA – Dunnett’s test. Data are presented as mean +/− SD. (b) Cytotoxicity assay was performed on day 4 after fetal liver MKs were treated with Triacsin C. Control=cells treated with the vehicle; positive control=cells lysed with TritonX-100. n = 4. Data are presented as mean +/− SEM (c) Fetal liver derived HSPCs were cultured with TPO and treated with the ACC inhibitor PF-05175175 at indicated doses. CD41+ and CD41/CD42d+ cells were quantified using flow cytometry, n = 5, one-way ANOVA – Dunnett’s test. Data are presented as mean +/− SD (d) Cytotoxicity assay was performed day 4 after fetal liver MKs treated with PF-05175175. Control=cells treated with the vehicle; positive control=cells lysed with TritonX-100. n = 4. Data are presented as mean +/− SEM (e) Fetal liver derived HSPCs were cultured with TPO and treated with the FASN inhibitor Cerulenin at indicated doses CD41+ and CD41/CD42d+ cells were quantified using flow cytometry, n = 5, one-way ANOVA – Dunnett’s test. Data are presented as mean +/− SD (f) Cytotoxicity assay was performed day 4 after fetal liver MKs were treated with Cerulenin. Control=cells treated with the vehicle; positive control=cells lysed with TritonX-100, n = 4. Data are presented as mean +/− SEM (g) Representative graph of mitostress assay. MKs were treated with Triacsin C (T4540, Sigma), PF-05175175 (PZ0299, Sigma), Cerulenin (C2389, Sigma) at the indicated concentrations in complete media for 90 min prior the measurement. Oxygen consumption rates were measured in accordance with manufacturer instructions (Agilent/Seahorse Bioscience) n = 5 biological replicates (h) Individual parameters for basal respiration, maximal respiration, and spare respiratory capacity were measured and analyzed. n = 5 biological replicates, one-way ANOVA. Data are presented as mean +/− SD.

Source data

Extended Data Fig. 3 Blood cell counts and characteristics in SFA-enriched high fat diet.

Blood parameters were measured using a Sysmex hematology analyzer, (a) Platelet Distribution Width (PDW), (b) Mean Platelet Volume (MPV), (c) red blood cells, (d) white blood cells, (e) neutrophils; WT n = 12 and Cd36−/− n = 16, animals per group, unpaired t-test (f) Platelet lifespan was assessed by flow cytometry measuring the fluorescence-positive platelet population at the indicated time points after injection of biotin-NHS. n = 5 animals per group, unpaired t-test. All data are presented as mean +/− SD.

Source data

Extended Data Fig. 4 Blood cell counts and characteristics in PUFA-enriched high fat diet.

Blood parameters were measured using a Sysmex hematology analyzer, (a) Platelet Distribution Width (PDW), (b) Mean Platelet Volume (MPV), (c) red blood cells; n = 10, unpaired t-test. (d) Platelet lifespan was assessed by flow cytometry measuring the fluorescence-positive platelet population at the indicated time points after injection of biotin-NHS. n = 5, unpaired t-test. Data are presented as mean +/− SD.

Source data

Extended Data Fig. 5 Candidate gene panel analysis.

Schematic of the bioinformatic pipeline workflow used for patient recruitment in the UK-GAPP study.

Extended Data Fig. 6 Mutant CD36 constructs are not trafficked to the cell surface.

WT and mutant CD36 constructs were transfected into both (a) Jurkat T cells and (b) HEK293 cells and surface expression was assessed. Flow cytometry scatter plots indicate that only wildtype CD36 is detected on the cell surface for both cell types. n = 3 biological replicates, unpaired t-test.

Extended Data Fig. 7 Gating strategies for flow cytometry panels.

In vitro studies with bone marrow and fetal liver MKs. Gating strategies of CD41+ and CD41/CD42d+ cells were analyzed using FlowJo. (b) Gating strategies to analyze hematopoietic stem cells and progenitors using FlowJo.

Extended Data Table 1 Thrombopoietin measurements in plasma from mice fed with chow, DIO and PUFA-enriched diets
Full size table
Extended Data Table 2 Summary of variants included in the UK-GAPP study
Full size table

Supplementary information

Reporting Summary

Supplementary Table 1

HPLC gradient.

Supplementary Table 2

Mass pairs used for identifying lipids.

Source data

Source Data Fig. 1

Raw data for bulk RNA-seq experiments, detailed normalized data for all lipid species identified in the lipidomics study and Statistical Source Data.

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Unprocessed western blots.

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Source Data Extended Data Fig./Table 1

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Barrachina, M.N., Pernes, G., Becker, I.C. et al. Efficient megakaryopoiesis and platelet production require phospholipid remodeling and PUFA uptake through CD36. Nat Cardiovasc Res 2, 746–763 (2023). https://doi.org/10.1038/s44161-023-00305-y

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