Human plasma proteomic profiles indicative of cardiorespiratory fitness

Jeremy M Robbins, Bennet Peterson, Daniela Schranner, Usman A Tahir, Theresa Rienmüller, Shuliang Deng, Michelle J Keyes, Daniel H Katz, Pierre M Jean Beltran, Jacob L Barber, Christian Baumgartner, Steven A Carr, Sujoy Ghosh, Changyu Shen, Lori L Jennings, Robert Ross, Mark A Sarzynski, Claude Bouchard, Robert E Gerszten, Jeremy M Robbins, Bennet Peterson, Daniela Schranner, Usman A Tahir, Theresa Rienmüller, Shuliang Deng, Michelle J Keyes, Daniel H Katz, Pierre M Jean Beltran, Jacob L Barber, Christian Baumgartner, Steven A Carr, Sujoy Ghosh, Changyu Shen, Lori L Jennings, Robert Ross, Mark A Sarzynski, Claude Bouchard, Robert E Gerszten

Abstract

Maximal oxygen uptake (VO2max) is a direct measure of human cardiorespiratory fitness and is associated with health. However, the molecular determinants of interindividual differences in baseline (intrinsic) VO2max, and of increases of VO2max in response to exercise training (ΔVO2max), are largely unknown. Here, we measure ~5,000 plasma proteins using an affinity-based platform in over 650 sedentary adults before and after a 20-week endurance-exercise intervention and identify 147 proteins and 102 proteins whose plasma levels are associated with baseline VO2max and ΔVO2max, respectively. Addition of a protein biomarker score derived from these proteins to a score based on clinical traits improves the prediction of an individual's ΔVO2max. We validate findings in a separate exercise cohort, further link 21 proteins to incident all-cause mortality in a community-based cohort and reproduce the specificity of ~75% of our key findings using antibody-based assays. Taken together, our data shed light on biological pathways relevant to cardiorespiratory fitness and highlight the potential additive value of protein biomarkers in identifying exercise responsiveness in humans.

Figures

Extended Data Fig. 1 |. Secreted proteins…
Extended Data Fig. 1 |. Secreted proteins positively related to bone homeostasis and baseline Vo2max.
Functional representation of proteins’ role in bone metabolism and homeostasis. Left and middle: SMOC1 regulates osteoblast differentiation. BMPs are related to bone formation via the TGF-β pathway and are mediated by extracellular signalling molecules such as NOG. Right: simplified schematic of proteins related to cartilage formation and their location within cartilage tissue.
Extended Data Fig. 2 |. Receiver-operating characteristic…
Extended Data Fig. 2 |. Receiver-operating characteristic curve for relative Vo2max changes with exercise training > 15% using overlapping targets between aptamer- and antibody-based proteomic platforms.
7/10 overlapping proteins on both platforms demonstrated moderate-strong correlation (SELE, TCL1A, COMP, CREG1, STC1, IL1RL2, LILRA2; ρ = 0.41–0.91) and were used in modeling.
Fig. 1 |. Proteins associated with baseline…
Fig. 1 |. Proteins associated with baseline Vo2max among offspring and parents.
Protein associations (FDR n = 433). We subsequently validated 50/94 proteins in the cohort of parents (n = 221; P < 0.05). Ninety of 94 proteins were directionally consistent, indicated by quadrant (increase in parents and offspring, upper right; decrease in parents and offspring, lower left).
Fig. 2 |. Plasma proteins associated with…
Fig. 2 |. Plasma proteins associated with baseline and ΔVo2max.
Protein relationships to baseline VO2max (ml O2 min−1) in a linear regression model adjusted for age, sex, BMI and race. The value of leptin’s relationship with baseline VO2max extends beyond the scale.
Fig. 3 |. Muscle proteins positively associated…
Fig. 3 |. Muscle proteins positively associated with baseline Vo2max.
Left, muscle filament depiction highlighting the proteins positively associated with baseline VO2max that participate in striated muscle structure and/or function. Myosin-binding protein slow-skeletal isoform (MYBPC1) regulates myosin–actin cross-bridge formation. Troponin I (TNNI2) inhibits actin-activated myosin ATPase activity. Gelsolin (GSN) is an actin severing protein. Myosin light-chain elements (MYL3 and MYL6B) regulate mechano-enzymatic function of myosin. Alpha-actinin 2 (ACTN2; not shown) and myomesin-2 (MYOM2) are actin and myosin stabilizing proteins. Right, table of names of proteins depicted at left and their associated gene symbols.
Fig. 4 |. GSEA for proteins associated…
Fig. 4 |. GSEA for proteins associated with ΔVo2max or baseline Vo2max.
Overview of overrepresented biological pathways and their connectivity using Cytoscape v3.7.1. a, Network visualization of GSEA results using the complete dataset of protein–ΔVO2max associations. Red dots indicate pathways with over-represented positive protein–ΔVO2max associations, and blue dots indicate over-represented negative protein–ΔVO2max associations. A larger circle size denotes a larger number of genes in a pathway, and darker shades indicate a higher degree of enrichment. Clusters indicate biological pathways with shared proteins and biological function. b–d, Selected clusters of biological pathways with annotation, from a; the top contributing proteins to enrichment score are shown in a table. e, Network visualization of GSEA results using the complete dataset of protein–baseline VO2max associations.
Fig. 5 |. ROC curves for relative…
Fig. 5 |. ROC curves for relative Vo2max changes with exercise training > 15%.
a, The clinical trait score (age, sex, BMI and race) had a modest AUC. b, Addition of the protein score significantly improved the AUC. The sensitivity, specificity, positive predictive value, negative predictive value and accuracy at the optimal cutoff are included.
Fig. 6 |. Spearman’s correlations between aptamer-based…
Fig. 6 |. Spearman’s correlations between aptamer-based and antibody-based assays among top findings.
Spearman’s correlations between protein levels measured by an aptamer-based method (log (RFU); x axis) and antibody-based method (log (NPX); y axis).

References

    1. Hawley JA, Hargreaves M, Joyner MJ & Zierath JR Integrative biology of exercise. Cell 159, 738–749 (2014).
    1. Hawkins MN, Raven PB, Snell PG, Stray-Gundersen J & Levine BD Maximal oxygen uptake as a parametric measure of cardiorespiratory capacity. Med. Sci. Sports Exerc. 39, 103–107 (2007).
    1. Ross R et al. Importance of assessing cardiorespiratory fitness in clinical practice: a case for fitness as a clinical vital sign: a scientific statement from the american heart association. Circulation 134, e653–e699 (2016).
    1. Myers J et al. Exercise capacity and mortality among men referred for exercise testing. N. Engl. J. Med. 346, 793–801 (2002).
    1. Mora S et al. Ability of exercise testing to predict cardiovascular and all-cause death in asymptomatic women: a 20-year follow-up of the lipid research clinics prevalence study. JAMA 290, 1600–1607 (2003).
    1. Blair SN et al. Changes in physical fitness and all-cause mortality. A prospective study of healthy and unhealthy men. JAMA 273, 1093–1098 (1995).
    1. Clausen JSR, Marott JL, Holtermann A, Gyntelberg F & Jensen MT Midlife cardiorespiratory fitness and the long-term risk of mortality: 46 years of follow-p. J. Am. Coll. Cardiol. 72, 987–995 (2018).
    1. di Prampero PE & Ferretti G Factors limiting maximal oxygen consumption in humans. Respir. Physiol. 80, 113–127 (1990).
    1. González-Alonso J & Calbet JA Reductions in systemic and skeletal muscle blood flow and oxygen delivery limit maximal aerobic capacity in humans. Circulation 107, 824–830 (2003).
    1. Wagner PD CrossTalk proposal: diffusion limitation of O2 from microvessels into muscle does contribute to the limitation of V̇O2max. J. Physiol. 593, 3757–3758 (2015).
    1. Joyner MJ & Coyle EF Endurance exercise performance: the physiology of champions. J. Physiol. 586, 35–44 (2008).
    1. Bouchard C et al. Familial resemblance for VO2max in the sedentary state: the HERITAGE family study. Med. Sci. Sports Exerc. 30, 252–258 (1998).
    1. Skinner JS et al. Age, sex, race, initial fitness, and response to training: the HERITAGE Family Study. J. Appl. Physiol. 90, 1770–1776 (2001).
    1. Williams CJ et al. Genes to predict VO. BMC Genomics 18, 831 (2017).
    1. Sarzynski MA, Ghosh S & Bouchard C Genomic and transcriptomic predictors of response levels to endurance exercise training. J. Physiol. 595, 2931–2939 (2017).
    1. Lewis GD et al. Metabolic signatures of exercise in human plasma. Sci. Transl. Med. 2, 33ra37 (2010).
    1. Overmyer KA et al. Maximal oxidative capacity during exercise is associated with skeletal muscle fuel selection and dynamic changes in mitochondrial protein acetylation. Cell Metab. 21, 468–478 (2015).
    1. Wewer Albrechtsen NJ et al. Plasma proteome profiling reveals dynamics of inflammatory and lipid homeostasis markers after Roux-En-Y gastric bypass surgery. Cell Syst. 12, 601–612(2018).
    1. Jacob J et al. Application of large scale aptamer-based proteomic profiling to ‘planned’ myocardial infarctions. Circulation 137, 1270–1277 (2017).
    1. Gold L et al. Aptamer-based multiplexed proteomic technology for biomarker discovery. PLoS ONE 5, e15004 (2010).
    1. Kim CH et al. Stability and reproducibility of proteomic profiles measured with an aptamer-based platform. Sci. Rep. 8, 8382 (2018).
    1. Williams SA et al. Plasma protein patterns as comprehensive indicators of health. Nat. Med. 25, 1851–1857 (2019).
    1. Ross R, Hudson R, Stotz PJ & Lam M Effects of exercise amount and intensity on abdominal obesity and glucose tolerance in obese adults: a randomized trial. Ann. Intern. Med. 162, 325–334 (2015).
    1. St Hilaire C et al. NT5E mutations and arterial calcifications. N. Engl. J. Med. 364, 432–442 (2011).
    1. Hasnain SZ et al. Glycemic control in diabetes is restored by therapeutic manipulation of cytokines that regulate beta cell stress. Nat. Med. 20, 1417–1426 (2014).
    1. Lee EJ et al. Fibromodulin: a master regulator of myostatin controlling progression of satellite cells through a myogenic program. FASEB J. 30, 2708–2719 (2016).
    1. Hynes RO & Naba A Overview of the matrisome — an inventory of extracellular matrix constituents and functions. Cold Spring Harb. Perspect. Biol. 4, a004903 (2012).
    1. Ngo D et al. Aptamer-based proteomic profiling reveals novel candidate biomarkers and pathways in cardiovascular disease. Circulation 134, 270–285 (2016).
    1. Ko D et al. Proteomics profiling and risk of new-onset atrial fibrillation: Framingham Heart Study. J. Am. Heart Assoc 8, e010976 (2019).
    1. Emilsson V et al. Co-regulatory networks of human serum proteins link genetics to disease. Science 361, 769–773 (2018).
    1. Whitham M et al. Extracellular Vesicles Provide a Means for Tissue Crosstalk during Exercise. Cell Metab. 27, 237–251.e4 (2018).
    1. Whittle AJ et al. BMP8B increases brown adipose tissue thermogenesis through both central and peripheral actions. Cell 149, 871–885 (2012).
    1. Pellegrinelli V et al. Adipocyte-secreted BMP8b mediates adrenergic-induced remodeling of the neuro-vascular network in adipose tissue. Nat. Commun. 9, 4974 (2018).
    1. Seldin MM et al. A strategy for discovery of endocrine interactions with application to whole-body metabolism. Cell Metab. 27, 1138–1155 (2018).
    1. Karsenty G & Olson EN Bone and muscle endocrine functions: unexpected paradigms of inter-organ communication. Cell 164, 1248–1256 (2016).
    1. Santos-Parker JR, Santos-Parker KS, McQueen MB, Martens CR & Seals DR Habitual aerobic exercise and circulating proteomic patterns in healthy adults: relation to indicators of healthspan. J. Appl. Physiol. 125, 1646–1659 (2018).
    1. Wang CY et al. Cardiorespiratory fitness levels among US adults 20–49 years of age: findings from the 1999–2004 national health and nutrition examination survey. Am. J. Epidemiol. 171, 426–435 (2010).
    1. Swift DL et al. Low cardiorespiratory fitness in African Americans: a health disparity risk factor? Sports Med. 43, 1301–1313 (2013).
    1. Fleg JL et al. Accelerated longitudinal decline of aerobic capacity in healthy older adults. Circulation 112, 674–682 (2005).
    1. Abe T, Loenneke JP & Thiebaud RS Fat-free adipose tissue mass: impact on peak oxygen uptake (VO2peak) in adolescents with obesity. Sports Med. 49, 9–15 (2019).
    1. Bray MS et al. The human gene map for performance and health-related fitness phenotypes: the 2006–2007 update. Med. Sci. Sports Exerc. 41, 35–73 (2009).
    1. Timmons JA et al. Using molecular classification to predict gains in maximal aerobic capacity following endurance exercise training in humans. J. Appl. Physiol. 108, 1487–1496 (2010).
    1. Bouchard C et al. Genomic predictors of the maximal O2 uptake response to standardized exercise training programs. J. Appl. Physiol. 110, 1160–1170 (2011).
    1. Ghosh S et al. Exploring the underlying biology of intrinsic cardiorespiratory fitness through integrative analysis of genomic variants and muscle gene expression profiling. J. Appl. Physiol. 126, 1292–1314 (2019).
    1. Ghosh S et al. Integrative pathway analysis of a genome-wide association study of (V)O2max response to exercise training. J. Appl. Physiol. 115, 1343–1359 (2013).
    1. Carvalho-Silva D et al. Open Targets Platform: new developments and updates two years on. Nucleic Acids Res. 47, D1056–D1065 (2018).
    1. Lee PS et al. Plasma gelsolin depletion and circulating actin in sepsis: a pilot study. PLoS ONE 3, e3712 (2008).
    1. Lee PS et al. Plasma gelsolin and circulating actin correlate with hemodialysis mortality. J. Am. Soc. Nephrol. 20, 1140–1148 (2009).
    1. Egerstedt A et al. Profiling of the plasma proteome across different stages of human heart failure. Nat. Commun. 10, 5830 (2019).
    1. Witke W et al. Hemostatic, inflammatory, and fibroblast responses are blunted in mice lacking gelsolin. Cell 81, 41–51 (1995).
    1. Lee WM & Galbraith RM The extracellular actin-scavenger system and actin toxicity. N. Engl. J. Med. 326, 1335–1341 (1992).
    1. Goetzl EJ et al. Gelsolin binding and cellular presentation of lysophosphatidic acid. J. Biol. Chem. 275, 14573–14578 (2000).
    1. Li GH, Arora PD, Chen Y, McCulloch CA & Liu P Multifunctional roles of gelsolin in health and diseases. Med Res. Rev. 32, 999–1025 (2012).
    1. Baird GS & Hoofnagle AN A novel discovery platform: aptamers for the quantification of human proteins. Clin. Chem. 63, 1061–1062 (2017).
    1. Bouchard C et al. The HERITAGE family study. Aims, design, and measurement protocol. Med. Sci. Sports Exerc. 27, 721–729 (1995).
    1. Skinner JS et al. Heart rate versus %VO2max: age, sex, race, initial fitness, and training response — HERITAGE. Med. Sci. Sports Exerc. 35, 1908–1913 (2003).
    1. Skinner JS et al. Reproducibility of maximal exercise test data in the HERITAGE family study. Med. Sci. Sports Exerc. 31, 1623–1628 (1999).
    1. Candia J et al. Assessment of variability in the SOMAscan assay. Sci. Rep. 7, 14248 (2017).
    1. Benson MD et al. The genetic architecture of the cardiovascular risk proteome. Circulation 137, 1158–1172 (2017).
    1. Suhre K et al. Connecting genetic risk to disease end points through the human blood plasma proteome. Nat. Commun. 8, 14357 (2017).
    1. Sun BB et al. Genomic atlas of the human plasma proteome. Nature 558, 73–79 (2018).
    1. Batista GEAPA & Monard MC An analysis of four missing data treatment methods for supervised learning. Appl. Artif. Intell. 17, 519–533 (2003).
    1. Tsamardinos I, Brown LE & Aliferis CF The max-min hill-climbing Bayesian network structure learning algorithm. Mach. Learn. 65, 31–78 (2006).
    1. Subramanian A et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA 102, 15545–15550 (2005).
    1. Merico D, Isserlin R, Stueker O, Emili A & Bader GD Enrichment map: a network-based method for gene-set enrichment visualization and interpretation. PLoS ONE 5, e13984 (2010).

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