Skeletal muscle nitrate storage – the missing piece of the nitrate supplementation puzzle?
Bibliographic record
Abstract
Over the last decade, nitrate (NO3−) supplementation – delivered as beetroot juice (BRJ) or sodium/potassium nitrate – has gained widespread attention for its potential as a therapeutic and ergogenic aid (Jones et al. 2018). Specifically, NO3− supplementation has been shown to reduce blood pressure and improve exercise performance (Jones et al. 2018). These benefits have been attributed to an increase in nitric oxide (NO) bioavailability following exogenous NO3− intake. NO is a widely expressed, transient and potent signalling molecule involved in the regulation of several physiological processes including haemodynamics, neurotransmission, mitochondrial function and cross-bridge cycling (Jones et al. 2018). NO can be produced in both the presence or absence of oxygen. In an oxygen-dependent manner, NO can be produced endogenously via the NO synthase enzyme. Conversely, NO can be formed via oxygen-independent processes, including the reduction of nitrite (NO2−) via deoxy-haem proteins (deoxyhaemoglobin, deoxymyoglobin) or the enzymes xanthine oxidoreductase (XOR) and aldehyde oxidase (AO). Additionally, oral bacteria residing on the dorsal surface of the tongue can reduce both endogenous and exogenous NO3− to NO2− (which can be subsequently reduced to NO) via the enterosalivary circulation (Jones et al. 2018) (Fig. 1). Recent evidence suggests that human skeletal muscle may serve as a key site for NO3− storage and metabolism and therefore, may be a novel potential regulator of NO bioavailability following NO3− intake (Nyakayiru et al. 2017). The recent publication by Wylie et al. (2019) in The Journal of Physiology aimed to determine if dietary NO3− could also increase skeletal muscle NO2− concomitant to increases in NO3− stores. To expand further, the investigators also determined whether both NO3− and NO2− stores are utilized during intense exercise. Moreover, skeletal muscle expression patterns were revealed for proteins related to NO3− metabolism. The study involved 13 young, healthy and recreationally active adults (8 males, 5 females), who ingested an acute 140 ml dose of NO3−-rich (12.8 mmol NO3−) or NO3−-depleted BRJ prior to performing an intensive exercise protocol that induced exhaustion in ∼6–7 min. Measurements based on multiple blood samples and skeletal muscle biopsies revealed that: (1) basal skeletal muscle NO3− concentrations were significantly higher than plasma concentrations; (2) BRJ supplementation significantly increased muscle NO3− stores with NO3− concentrations remaining higher in muscle compared to plasma (2 h post-ingestion); (3) skeletal muscle expressed proteins related to the transport (sialin) of NO3− from plasma and its subsequent reduction to NO2− and NO (XOR and AO proteins); (4) following BRJ supplementation, excess NO3− stored in skeletal muscle was significantly reduced after an exhaustive, high intensity exercise bout. Given the multi-faceted effects of NO3− species (NO3−, NO2− and NO) on haemodynamics as well as on central and peripheral tissue metabolism, the maintenance of these compounds in systemic circulation is important, particularly during the increased demands of exercise. The delivery of NO3− and its uptake into salivary glands represents a key step in its clearance, and it was only recently established that a protein-mediated transport mechanism is responsible for this phenomenon (Piknova et al. 2016). Specifically, the membrane-localized expression of sialin in the salivary glands highlights a crucial component in the maintenance of plasma NO3−–NO2−–NO. The role of skeletal muscle in regulating the relative balance of NO3− species following NO3− supplementation has remained largely unexplored. The study by Wylie et al. (2019) showed that the NO3− transporter sialin is expressed in whole-muscle homogenates from young, healthy human participants, further supporting the role of skeletal muscle in the uptake and storage of exogenous NO3− (Fig. 1). This notion is supported by the observation of a 5-fold increase in skeletal muscle NO3− and a 3-fold rise in NO2− 2 h after BRJ ingestion. While a role for sialin in mediating NO3− uptake into muscle is strongly supported in the current study, the extent of its contribution to the observed increases in muscle NO3− stores is unclear. It is intriguing to consider the relationship between the quantitative protein expression of sialin (particularly at the sarcolemmal membrane) and the observed changes in skeletal muscle NO3− stores. Furthermore, it may be of interest to determine the time-course to achieve peak NO3− stores in healthy human muscle. In the present study, skeletal muscle NO3− stores were determined ∼2 h post-supplementation, coinciding with peak muscle NO3− stores previously shown in individuals with type 2 diabetes (measured 2, 4, 7 h post-ingestion) following the ingestion of 12.8 mg/kg sodium nitrate (Nyakayiru et al. 2017). However, plasma NO3− concentrations are known to peak ∼1 h post-supplementation and remain elevated above baseline concentrations for the subsequent 24 h in healthy humans (Jones et al. 2018). Therefore, a temporal investigation may provide insight into the muscle's potential role as a whole-body regulator of blood NO3− species and the concentration gradient that exists between muscle and plasma. Undoubtedly, more work is required to unravel the signalling mechanisms by which sialin is expressed and potentially recruited to the sarcolemmal membrane to take up NO3− from the circulation, and how this may contribute to the timing of NO3− storage. Clearly, a growing body of evidence supports the role of skeletal muscle as a storage reservoir for ingested NO3− and it is likely that these stores have important implications. To observe the physiological responses associated with exogenous NO3− supplementation, stored NO3− must be converted to bioactive NO2− and NO, implying that the intramuscular machinery involved in NO3− metabolism and utilization may be an essential piece of the NO3− supplementation puzzle. Previous work in rodents has identified a role for XOR (and perhaps AO) in the stepwise reduction of NO3− to NO2−/NO across a variety of tissues (Piknova et al. 2016). In the current study, Wylie et al. (2019) reported that human skeletal muscle expresses XOR and AO, implicating these proteins as potential regulators in the conversion of NO3− stores to NO2−/NO during high intensity exercise. Importantly, when pre-exercise muscle NO3− stores were elevated through the ingestion of BRJ, a subsequent reduction following intense exercise was observed (Fig. 1). This decline in muscle NO3− during exercise follows previous work showing that resting muscle NO3− can remain elevated above baseline 7 h post-ingestion of sodium nitrate in humans (Nyakayiru et al. 2017). Thus, the observed decrease in human muscle NO3− in the current study was likely exercise-induced. Interestingly, the potential mechanism(s) governing this process appears to be only present with elevated pre-exercise NO3− muscle stores. Taken together, these observations suggest that human skeletal muscle metabolizes NO3− during exercise, and XOR and AO may facilitate this process. The present study also found a non-significant decline in muscle NO2− immediately following the high intensity exercise bout. This finding contrasts with previous rodent data (Piknova et al. 2016) that demonstrated an increase in muscle NO2− following exercise (likely due to the reduction of stored NO3− to NO2−). Wylie et al. (2019) speculated that the slight drop in muscle NO2− may be explained by a greater rate of NO2− to NO reduction compared to NO3− to NO2− reduction, which may be facilitated by a relatively acidic and hypoxic skeletal muscle environment immediately following high intensity anaerobic exercise as well as a greater number of NO2− to NO reduction pathways compared to NO3− to NO2−. Follow-up work is required to determine the exact rates of turnover for NO3− species in skeletal muscle both at rest and in response to an exercise stimulus. An issue receiving increasing attention is the considerable inter-individual variability that exists in responses to nutrition and exercise interventions. Data from the current investigation demonstrated large variability between participants in baseline muscle NO3− stores, the rise in muscle NO3− following BRJ supplementation, and NO3− utilization during the exhaustive bout of high intensity exercise. These differences are corroborated by significant variability in sialin, XOR and AO protein contents. Though speculative, it is possible that more robust intramuscular responses to exogenous NO3− supplementation may contribute to the pronounced physiological responses observed in specific demographics (i.e. recreationally active males versus endurance-trained individuals). As such, it may be important to characterize the population-specific differences in skeletal muscle sarcolemmal membrane sialin content/transporter activity and the ability of XOR/AO to reduce these stores to bioactive NO2−/NO. These factors could potentially underpin any observed differences in skeletal muscle NO3− uptake and storage, as well as the therapeutic or exercise performance effects of exogenous NO3−. Within the current study, it may also be important to consider the participant cohort, which consisted of both males (n = 8) and females (n = 5). It is possible that sex-based differences, such as skeletal muscle fibre type composition and mass, may influence the absolute and temporal nature of NO3− storage and its subsequent metabolism in skeletal muscle (Wickham & Spriet, 2019). In conclusion, skeletal muscle should be considered a significant reservoir for the coordinated storage, metabolism and utilization of NO3− following exogenous NO3− intake and during exercise. The ongoing research pertaining to dietary NO3− supplementation and skeletal muscle metabolism will undoubtedly spark a plethora of future research endeavours. None declared. All authors have approved the final version of the manuscript and agree to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. All persons designated as authors qualify for authorship, and all those who qualify for authorship are listed. K.A.W. (Canada Graduate Scholarships – Doctoral program) and D.T.C. (Postgraduate Scholarships – Doctoral program) hold scholarships from the Natural Science and Engineering Research Council of Canada (NSERC). D.G.M. holds a 2019 Ontario Graduate Scholarship. J.N. is the recipient of the 2019 Naomi Cermak Memorial Graduate Travel Award. The authors would like to thank Dr Lawrence Spriet, Dr Luc van Loon and Dr Martin Gibala for editing the manuscript.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".