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Identification of a leucine-mediated threshold effect governing macrophage mTOR signalling and cardiovascular risk

Nature Metabolism volume 6, pages 359–377 (2024)Cite this article

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Abstract

High protein intake is common in western societies and is often promoted as part of a healthy lifestyle; however, amino-acid-mediated mammalian target of rapamycin (mTOR) signalling in macrophages has been implicated in the pathogenesis of ischaemic cardiovascular disease. In a series of clinical studies on male and female participants (NCT03946774 and NCT03994367) that involved graded amounts of protein ingestion together with detailed plasma amino acid analysis and human monocyte/macrophage experiments, we identify leucine as the key activator of mTOR signalling in macrophages. We describe a threshold effect of high protein intake and circulating leucine on monocytes/macrophages wherein only protein in excess of ∼25 g per meal induces mTOR activation and functional effects. By designing specific diets modified in protein and leucine content representative of the intake in the general population, we confirm this threshold effect in mouse models and find ingestion of protein in excess of ∼22% of dietary energy requirements drives atherosclerosis in male mice. These data demonstrate a mechanistic basis for the adverse impact of excessive dietary protein on cardiovascular risk.

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Fig. 1: Very high protein intake induces mTORC1 activation and inhibition of autophagy in isolated human monocytes.
Fig. 2: High-protein intake induces mTORC1 activation and inhibition of autophagy in isolated human monocytes.
Fig. 3: Plasma amino acid concentration profiles after low, standard, high and very high protein intake.
Fig. 4: Identification of leucine as the most consequential amino acid for mTORC1 activation in HMDMs.
Fig. 5: Leucine-mediated mTORC1 activation and downstream sequela in HMDMs.
Fig. 6: Leucine is the most consequential amino acid for mTORC1 activation in murine monocytes/macrophages in vitro and in vivo.
Fig. 7: Impact of varying dietary protein intake on atherosclerosis in a mouse model.
Fig. 8: Effects of dietary protein and leucine intake on macrophage mTORC1 signalling and atherosclerotic cardiovascular risk.

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Data availability

All source data used to generate the figures are available as part of the manuscript files and from the corresponding authors upon reasonable request. Source data are provided with this paper.

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Acknowledgements

We thank T. Roediger, the staff of the Center for Human Nutrition at Washington University School of Medicine and staff at the CTRU for assistance in conducting the metabolic studies and their technical assistance in processing the study samples. We also thank the study participants for their participation. Funding. This work was supported by National Institutes of Health grants R01 HL125838 (to B.R.), R01 HL159461 (to B.R. and B.M.), R01 DK121560 (to B.M.), R01 DK131188 (to B.R., B.M. and J.D.S.), P30 DK056341 (Washington University Nutrition and Obesity Research Center), P30 DK020579 (Washington University Diabetes Research Center), UL1 TR000448 (Washington University School of Medicine Institute of Clinical and Translational Sciences) and T32 HL134635 (to A.R.) and grants from the US Department of Veterans Affairs MERIT I01 BX003415 (to B.R.), American Diabetes Association (1-18-IBS-029 to B.R.) and the Longer Life Foundation (to B.M.). The funding sources had no role in the design and conduct of the study; collection, management, analysis and interpretation of the data; preparation, review or approval of the manuscript; and decision to submit the manuscript for publication.

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Author notes

  1. These authors contributed equally: Xiangyu Zhang, Divya Kapoor.

Authors and Affiliations

  1. Department of Medicine and Vascular Medicine Institute, University of Pittsburgh School of Medicine and UPMC, Pittsburgh, PA, USA

    Xiangyu Zhang, Yu-Sheng Yeh & Babak Razani

  2. Pittsburgh VA Medical Center, Pittsburgh, PA, USA

    Xiangyu Zhang, Yu-Sheng Yeh & Babak Razani

  3. Cardiovascular Division, Washington University School of Medicine, St Louis, MO, USA

    Divya Kapoor, Se-Jin Jeong, Ismail Sergin, Eman Yousif, Astrid Rodriguez-Velez, Arick Park, Joel D. Schilling, Abhinav Diwan, Nathan O. Stitziel & Ali Javaheri

  4. John Cochran VA Medical Center, St Louis, MO, USA

    Divya Kapoor, Abhinav Diwan & Ali Javaheri

  5. Division of Nutritional Science and Obesity Medicine, Washington University School of Medicine, St. Louis, MO, USA

    Alan Fappi, Vasavi Shabrish & Bettina Mittendorfer

  6. Departments of Medicine and Nutrition and Exercise Physiology, University of Missouri, Columbia, MO, USA

    Alan Fappi, Vasavi Shabrish & Bettina Mittendorfer

  7. Division of Endocrinology, Metabolism and Lipid Research, Washington University School of Medicine, St Louis, MO, USA

    Jeremiah Stitham & Irfan J. Lodhi

  8. Department of Molecular and Cellular Physiology, Louisiana State University Health Sciences Center, Shreveport, LA, USA

    Arif Yurdagul Jr & Oren Rom

  9. Peter Munk Cardiac Center and University Health Network, University of Toronto, Toronto, Canada

    Slava Epelman

  10. Department of Pediatrics, Washington University School of Medicine, St Louis, MO, USA

    Marco Sardiello

  11. Division of Hematology, Department of Medicine, Washington University School of Medicine, St Louis, MO, USA

    Jaehyung Cho

  12. Department of Pathology and Immunology, Washington University School of Medicine, St Louis, MO, USA

    Jaehyung Cho

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  1. Xiangyu Zhang
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Contributions

X.Z., D.K., B.M. and B.R. designed the study. X.Z., D.K., S.J.J., A.F., J.S., V.S., I.S., E.Y., A.R., Y.S.Y., A.P., A.Y., O.R., S.E., J.D.S., M.S., A.D., J.C., N.O.S., A.J., I.J.L., B.M. and B.R. contributed to data acquisition, data analysis and data interpretation. All authors contributed to the writing of the paper. B.R. is the guarantor of this work, had full access to all the data in the study and assumes full responsibility for the integrity of the data and the accuracy of the data analysis.

Corresponding authors

Correspondence to Bettina Mittendorfer or Babak Razani.

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Nature Metabolism thanks Andreas Pfeiffer and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Ashley Castellanos-Jankiewicz, in collaboration with the Nature Metabolism team.

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Extended data

Extended Data Fig. 1 -for Main Fig. 1&2.

Extended Data Fig. 1 (A) Study #1 evaluated the effect of consuming extremes of protein intake as liquid meals (10% kcal protein vs 50% kcal protein) on monocyte mTORC1 activation and downstream functional events. Study #2 focused on a “real-world” setting and evaluated the effects of a standard mixed meal and a high-protein mixed meal (15% kcal vs 22% kcal as protein) on the same outcomes. Blood samples from participants were obtained at baseline and at defined time intervals after meal consumption, plasma was isolated for evaluating amino acid mass concentration and CD14 + /CD16− monocytes were isolated for mTORC1 signalling evaluation using Western blotting (WB), immunofluorescence microscopy (IF) and fluorescence-activated cell sorting (FACS). (B) Representative FACS pictures showing high efficiency of circulating monocytes isolation from participants’ blood. (C) Plasma triglyceride (TG) concentration before and after participants (n = 12) consumed a very high protein (50% kcal) or a low-protein (10% kcal) liquid meal (time as indicated). (D) Representative FACS pictures for Fig. 2d. (E) Quantification of mTOR-LAMP2 colocalization in isolated circulating monocytes participants (n = 5) consumed a very high protein (50% kcal) or a low-protein (10% kcal) liquid meals (time as indicated). P < 10e-8 and determined by Two-way ANOVA with Sidak’s multiple comparisons. Representative pictures on the right. (F) Plasma TG concentration before and after participants (n = 9) consumed a standard mixed meal (15% kcal protein) or a high protein (22% kcal protein) mixed meal (time as indicated). For all graphs, data are presented as mean ± SEM. ***P < 0.001.

Extended Data Fig. 2 -for Main Fig. 1.

Extended Data Fig. 2 Plasma concentrations of 20 amino acids before and after participants (n = 12) consumed a very high protein (50% kcal) or a low-protein (10% kcal) liquid meal (time as indicated). For all graphs, data are presented as mean ± SEM.

Extended Data Fig. 3 -for Main Fig. 2.

Extended Data Fig. 3 Plasma concentrations of 20 amino acids before and after participants (n = 9) consumed a standard mixed meal (15% kcal protein) or a high protein (22% kcal protein) mixed meal (time as indicated). For all graphs, data are presented as mean ± SEM.

Extended Data Fig. 4 -for Main Fig. 4.

Extended Data Fig. 4 (A) Representative images of cultured human monocytes-derived macrophages (HMDMs) from isolated human circulating monocytes before and after 12 days of differentiation. The picture represents for three independent experiments. (B-C) FACS (B) and immunofluorescence (C) analysis of differentiated HMDMs by macrophage markers CD11b, CD18 and CD68. The picture represents for three independent experiments. (D) Western blot analysis of mTOR activation in HMDM (using phospho-S6 and –S6K) after treatment with leucine (2 mM) or full amino acids. The picture represents for three independent experiments. (E-F) Immunofluorescence microscopy analysis and quantification of mTORC1 activation (using phospho-S6) (E) and autophagy inhibition (using LC3 puncta formation) (F) in HMDMs treated with 2 mM leucine. The IF data (E and F) were obtained from three independent experiments with analysis of n ≥ 15 cells per experiment (n = 45 in Group –aa and n = 40 in Group Leu for E; n = 58 in Group –aa and n = 58 in Group Leu for F). P < 10e-8 for E and F. (G) Representative images of LC3 in HMDMs for Fig. 5d. (H) Immunoblot analysis of the phosphorylation of AMPK and ULK1 in HMDMs by the selected amino acids (applied at 2 mM). n = 4 independent experiments per group. P = 0.00132 and determined by One-way ANOVA with Dunnett’s multiple comparisons. (I) Representative images of LC3 in HMDMs for Fig. 5g. For all graphs, data are presented as mean ± SEM, **P < 0.01 and ***P < 0.001.

Source data

Extended Data Fig. 5 -for Main Fig. 5.

Extended Data Fig. 5-for Main Fig. 5. (A) Western blot analysis of the dose-dependent effect of leucine on mTORC1 activation (using phospho-S6K) in human monocyte-derived macrophages (HMDMs). The picture represents for three independent experiments. (B) Representative FACS plots for Fig. 5e.

Source data

Extended Data Fig. 6 −for Main Fig. 6.

Extended Data Fig. 6 (A) Plasma concentrations of 20 amino acids before and after male mice were gavaged with a solution that contained 1.6 g/kg protein or vehicle (times as indicated). n = 3 mice for each group. (B) FACS analysis of mTORC1 activation in blood monocytes (using phosphorylated S6) from female mice gavaged with the same protein solution compared with vehicle (times as indicated). n = 4 mice for each group. P = 0.00829 and determined by Two-way ANOVA with Sidak’s multiple comparisons. For all graphs, data are presented as mean ± SEM. **P < 0.01.

Extended Data Fig. 7 Main Figs. 7 and 8.

Extended Data Fig. 7-for (A) Body composition (fat and lean weights) of ApoE-/- mice fed Western diets with low-protein (LP WD), moderate-protein (MP-WD), or high protein (HP-WD) contents for 8 weeks. n = 5 mice for each group. P = 0.00057. (B-C) Serum cholesterol (B) and triglyceride (C) concentrations in ApoE-/- mice before and after placement on low, moderate, or high protein Western diets for 8 weeks. n = 5 mice for each group. (D-E) Serum cholesterol (D) and triglyceride (E) concentrations after ApoE-/- mice were fed six different diets with varying protein and leucine contents, including: 1) a moderate-protein Western Diet (MP-WD, n = 10), 2) a high-protein Western Diet (HP-WD, n = 7), 3) a MP-WD to which an amount of leucine, but no other amino acids, was added to match the leucine content of the HP-WD (MP-WD+Leu, n = 10), 4) a MP-WD to which amino acids were added to match the total amino acid content of the HP-WD (MP-WD + AA, n = 10), 5) a MP-WD to which amino acids, except for leucine, were added to match the content of all amino acids, except for leucine, of the HP-WD (MP-WD + AA (Normal Leu), n = 15) and 6) an isonitrogenous MP-WD + AA-Leu diet (MP-WD + AA (Normal Leu (IsoN), n = 15) which consisted of the MP-WD + AA-Leu diet with additional amino acids to match the total amino acid nitrogen content of the HP WD. (F-H) Plaque composition quantified by immunofluorescence (IF) microscopy of aortic root sections for (F) macrophages (MOMA-2), (G) apoptosis (TUNEL + ) and (H) necrotic core (MP-WD, n = 10; HP-WD, n = 7; MP-WD+Leu, n = 10; MP-WD + AA, n = 10; MP-WD + AA(Normal Leu), n = 15; isoMP-WD + AA, n = 15 biologically independent animals). P = 0.0159 for F and P = 0.000329, 0.02219 and 0.00417 for G. For all graphs, data are presented as mean ± SEM and determined by One-way ANOVA with Dunnett’s multiple comparisons. *P ≤ 0.05, **P < 0.01 and ***P < 0.001.

Extended Data Fig. 8 CONSORT diagram of participant enrolment in Trials.

(A,B) CONSORT diagram of participant enrolment in Trial NCT03946774 (A) and Trial NCT03994367 (B).

Extended Data Fig. 9 Gating strategies for FACS analysis.

Gating strategy for determination of parameters in human (A) and mouse circulating monocytes (B). (A) Dead cells and cell aggregates were removed in (a) and (b) to enrich single live cells. (c) Human leucocytes were selected using CD45. (d) Human circulating monocytes were enriched by CD11b and F4/80 gating and used for further analysis. (B) Dead cells and cell aggregates were removed in (a) and (b) to enrich single live cells. (c) Mouse leucocytes were selected using CD45. (d) Mouse circulating monocytes were enriched by CD11b and Ly-6c gating and used for further analysis.

Supplementary information

Supplementary Information

Supplementary Table 1, clinical study protocol 1-2, CONSORT checklist, Reporting on sex and gender.

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Source Data Fig. 1

Unprocessed western blots.

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Unprocessed western blots.

Source Data Fig. 3

Unprocessed western blots.

Source Data Fig. 4

Unprocessed western blots.

Source Data Fig. 5

Unprocessed western blots.

Source Data Extended Data Fig. 4

Unprocessed western blots.

Source Data Extended Data Fig. 5

Unprocessed western blots.

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Zhang, X., Kapoor, D., Jeong, SJ. et al. Identification of a leucine-mediated threshold effect governing macrophage mTOR signalling and cardiovascular risk. Nat Metab 6, 359–377 (2024). https://doi.org/10.1038/s42255-024-00984-2

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