Body tissues are generally 15N-enriched over the diet, with a discrimination factor (Δ15N) that varies among tissues and individuals as a function of their nutritional and physiopathological condition. However, both 15N bioaccumulation and intra- and inter-individual Δ15N variations are still poorly understood, so that theoretical models are required to understand their underlying mechanisms. Using experimental Δ15N measurements in rats, we developed a multi-compartmental model that provides the first detailed representation of the complex functioning of the body' s Δ15N system, by explicitly linking the sizes and Δ15N values of 21 nitrogen pools to the rates and isotope effects of 49 nitrogen metabolic fluxes. We have shown that (i) besides urea production, several metabolic pathways (e. g. , protein synthesis, amino acid intracellular metabolism, urea recycling and intestinal absorption or secretion) are most probably associated with isotope fractionation and together contribute to 15N accumulation in tissues, (ii) the Δ15N of a tissue at steady-state is not affected by variations of its P turnover rate, but can vary according to the relative orientation of tissue free amino acids towards oxidation vs. protein synthesis, (iii) at the whole-body level, Δ15N variations result from variations in the body partitioning of nitrogen fluxes (e. g. , urea production, urea recycling and amino acid exchanges), with or without changes in nitrogen balance, (iv) any deviation from the optimal amino acid intake, in terms of both quality and quantity, causes a global rise in tissue Δ15N, and (v) Δ15N variations differ between tissues depending on the metabolic changes involved, which can therefore be identified using simultaneous multi-tissue Δ15N measurements. This work provides proof of concept that Δ15N measurements constitute a new promising tool to investigate how metabolic fluxes are nutritionally or physiopathologically reorganized or altered. The existence of such natural and interpretable isotopic biomarkers promises interesting applications in nutrition and health.
Nitrogen (N) metabolism in the body involves a complex network of various between- and within-tissues fluxes, consisting in the transport and/or transformation of various N compounds such as proteins (P), amino acids (AA), urea and ammonia. These various N transfers and metabolic processes are critical to the tissue assimilation of dietary N, the elimination of some dietary and endogenous N (mostly as urinary urea) and continuous exchanges of N compounds between different body compartments, thus ensuring a body N balance and P homeostasis. In particular, within each tissue, P synthesis and degradation fluxes ensure a continuous turnover of the P mass, which is essential to preserve lean mass and the numerous vital functions of body P. Moreover, free AA are constantly exchanged between tissues and used for P synthesis within tissues where they also enter the transamination and deamination pathways (leading to the production of urea that is mostly excreted in urine) as well as other secondary metabolic pathways. All these N fluxes are closely coordinated and their regulation implies changes in their amplitude, orientation or distribution, in both the short term, in order to deal with the discontinuous dietary intakes (with daily cycles of fed and fasted states), and, in the long term, to adapt to changing nutritional or physiological conditions. However, dysregulation of this complex system of N fluxes may occur, involving metabolic perturbations, reorientations or imbalances and possibly leading to altered P homeostasis. Globally, little is still known about the inter-tissue N fluxes. The data available on different N fluxes are fragmented and dispersed because the classical investigative methods, mostly based on the administration of stable isotope-labelled metabolic tracers, usually focus on determining a specific type of flux (e. g. , the administration of labelled AA to determine P synthesis rates, of labelled urea to study urea production and recycling fluxes, or of labelled dietary P to determine absorption kinetics, etc.) [1]–[4]. Because of these methodological limitations, we still have a poor understanding of the complex network of N metabolic fluxes between and within tissues, how they are coordinated and regulated in standard conditions and how they may be modulated as a function of changes in nutritional conditions (e. g. , P intake) or dysregulated during a drift towards a pathological state. It is therefore necessary to develop new approaches that will provide an integrated insight into the whole-body distribution, partitioning and possible reallocations of N fluxes. In parallel, a large body of evidence, mostly from ecological and archaeological studies, suggests that the N stable isotope compositions of metabolic pools (δ15N, the natural relative abundance of the rare stable isotope of N) are not only dependent on the δ15N of the diet but are also closely related to N metabolism and its modulations [5]. It is indeed well known that animal and human tissues are generally 15N-enriched relative to their diet, and several studies have reported that the extent of this tissue-to-diet 15N discrimination (Δ15N) varies between tissues in the same individual and may also differ between individuals depending on their particular nutritional or physiological conditions [5]–[8]. Such a δ15N trophic shift, and intra- and inter-individual Δ15N variations, are likely to result from the existence of isotope fractionation associated with certain metabolic pathways (e. g. , deamination or transamination) that are sensitive to the isotope mass and use the two N isotopes at distinct rates [9]. When located at a metabolic branch point, isotope effects should theoretically lead to isotopic discrimination between the substrates and the competing products, with metabolic products having distinct isotopic values depending on the fractionation extent of the metabolic pathway from which they originate and the relative amplitude of the fluxes into the competing pathways [10]–[13]. Variations in the Δ15N of metabolic pools under specific nutritional or physiological conditions may therefore reflect underlying modulations in N fluxes, and, in particular, changes in the relative proportion of catabolism and anabolism and in the N balance [11], [14]–[16]. This remains however a general concept and the principal factors responsible for Δ15N and its variations, as well as the integrated functioning of the body Δ15N system, are still poorly understood. This raises important questions as to which metabolic processes are actually fractionating, how Δ15N differences between tissues are established and what relationships exist between variations in metabolic fluxes and actual Δ15N values [11], [17]. Furthermore, because of the complexity and intricacy of the metabolic pathways from which they originate, variations in Δ15N values cannot be interpreted directly in terms of the underlying rearrangements of body N fluxes that they might indicate. Mechanistic modeling approaches are therefore necessary to clarify the fractionating processes which lead to between-tissue Δ15N differences and to determine which variations in metabolic fluxes can explain between-subject Δ15N differences. This study proposes a new approach that combines measurements of the size and Δ15N values of numerous N metabolic pools in rats and their analysis by compartmental modeling. We first developed a multi-compartmental model which reproduces AA and P metabolism in the whole body and which is consistent with previous data in the literature regarding the principal N metabolic flux values (absorption, excretion, urea production, P turnover, etc.) and known structural and dynamic system characteristics (metabolic compartmentation, differences in P turnover between tissues, etc.). Second, we showed that this model is able to reproduce the Δ15N variations observed experimentally among tissues and N fractions and thus to identify the fractionating processes that are responsible for such variations. Finally, model simulations enabled a clearer understanding of which flux modulations can impact tissue Δ15N values and, inversely, which, and in what way, Δ15N variations can reveal differences or rearrangements in N fluxes.
All sampled body N fractions were 15N enriched compared to the diet, except for the muscle free AA fraction and body urea, which were 15N depleted, and urinary urea, which had a δ15N similar to that of the diet. The pool-to-diet discrimination values (Δ15Npool = δ15Npool−δ15Ndiet) displayed major variations between the different sampled tissues and N fractions, with values ranging from −1. 5 to 5‰ (Figure 1). Interestingly, in all the tissues sampled, P fractions had higher Δ15N values than non-P fractions, with differences that varied from tissue to tissue: they were for instance larger in muscle and heart and smaller in the kidney Δ15NP−Δ15NAA = 4. 8‰, 4. 1‰ and 0. 6‰, respectively). Using the calibrated model, we performed model simulations to identify which flux modulations could explain and induce the Δ15N variations observed under specific nutritional or physiological conditions, and also conversely to determine which information could be inferred from Δ15N variations relative to underlying modifications in N metabolism. We assumed that ε factors were intrinsic parameters of the metabolic pathways so that they would not be impacted by changes in N fluxes induced by changes in conditions. Therefore, in this simulation phase, we only modified the values of the parameters k, to study the consequences of N flux value changes on tissue Δ15N. Model simulations were performed while changing the model fluxes either individually (as reported in this section) or under certain specific combinations (as reported in the next section), so as to mimic the principal types of probable flux modulations occurring in response to changes in the physiological or nutritional conditions (see the Material and Methods section for details on these model simulations). We first investigated the effect of individual changes in each of the main classes of N fluxes (i. e. , tissue P turnover, tissue AA oxidation or AA transfers between plasma and tissues). We simulated tissue Δ15N trajectories in response to these specific changes in cases where the N amounts in compartments either evolved (Simulation #1 with distinct initial and final elemental steady states, Figure 3A–D) or remained constant as a result of flux compensations re-establishing homeostasis (Simulation #2 with similar initial and final elemental steady states, Figure 3E–H). As a general rule, the model predicted that variations of fluxes located in a catenary structure with bidirectional exchanges (e. g. , the bidirectional fluxes of P synthesis and degradation) only transiently affected Δ15N values between two successive elemental steady states. More specifically, we predicted that modifications of the P turnover rates in tissues would not change the isotopic signatures of tissue P at steady state. Indeed, asynchronous variations in the tissue P synthesis and degradation fluxes, which impact tissue P size, induced only temporary changes in tissue AA and P Δ15N values during the transient phase of tissue P accretion or depletion, with Δ15N values returning to their initial levels at the final isotopic steady state (Simulation #1, Figure 3A–D). For instance in muscle, decreasing (or increasing) the P synthesis flux by decreasing (or increasing) the P synthesis capacity (ksM) induced a progressive decrease (or increase) in the P pool size and secondarily in the P degradation flux, until a new elemental steady state was reached. The transition between two distinct elemental steady states, during which an imbalance between the P synthesis and degradation fluxes created a P net loss (or gain), was associated with a transient increase (or decrease) in the P Δ15N value. Moreover, we predicted the same variations in the P mass and Δ15N values with an increase (or decrease) in the P degradation capacity (kdM) than with a decrease (or increase) in ksM. More generally, the model predicts that in any tissue, a P accretion (or depletion) is associated with a temporary decrease (or increase) in P Δ15N. It should be noted that in all cases, the Δ15N variations predicted were quantitatively small (Δ15N variations of only 0. 1‰ for 20% changes in the P mass). By contrast, variations of fluxes located at a metabolic branchpoint that induced permanent changes in the allocation of fluxes between competitive pathways (e. g. , changes in the relative orientation of tissue AA towards oxidation vs. P synthesis, or changes in AA transfers between plasma and tissues) were predicted to result in more marked and, above all, persistent variations in Δ15N values, even without any variation in the elemental steady state. For instance, an increase in hepatic AA oxidation (counterbalanced by an increase in urea recycling in order to maintain homeostasis) induced a progressive and lasting increase in hepatic P Δ15N, with higher Δ15N values at the final than at the initial isotopic steady state (Simulation #2, Figure 3E–H). Globally, in terms of the flux variations that effectively impact tissue Δ15N values, our results can be summarized by considering changes in the tissue p ratio. The p ratio corresponds to an integrated index of the relative metabolic utilization of tissue free AA that we defined as the proportion of AA that are directed toward oxidation rather than used for net P synthesis or exported to the circulation (p = fox/ (fox+fout+fs−fd), Figures 3C and G). We indeed demonstrated that differences in the Δ15N of a given tissue between two steady states resulted from permanent changes in the p ratio, whereas changes in the bidirectional fluxes of P synthesis and degradation induced only transient changes in the p ratio and did not modify steady-state Δ15N values. The significance of p to steady-state Δ15N values was also analytically noticeable from the model equations (Table S1). For instance, the Δ15N steady-state value in muscle P (Δ15NMP) depends on only two factors, the Δ15N of the plasma AA supplied to muscle (Δ15NPlAA) and this integrated index p (since Δ15NMP = Δ15NPlAA−p*εOX−εS, with εOX and εS being the fractionation factors for AA oxidation and P synthesis, respectively). Furthermore, when a metabolic change in a given tissue modifies its own P and AA Δ15N values, outward transport of this tissue AA consequently modifies the plasma AA Δ15N value and can thus also secondarily modify the Δ15N values of other body tissues, even if there are no associated metabolic changes in these tissues. For instance, after a concomitant increase in splanchnic oxidation and urea recycling, our model predicted that all body tissue Δ15N values were changed and that the Δ15N trajectories and the time required for tissue P to reach the new isotopic steady state varied between tissues (Figure S3), depending on their specific rates of plasma AA uptake and P turnover (Table S4 for the characteristic times required to reach 50% and 95% of isotopic equilibrium in each tissue). A growing body of evidence in the literature suggests that the isotopic signatures of tissues vary according to the quality and quantity of dietary N intake. More specifically, several studies have reported an increase in tissue Δ15N with a decrease in dietary P quality (i. e. , when the qualitative and/or quantitative supply of AA in dietary P do not match the AA demand of the individual) [8], [13], [33]–[37] and in cases of dietary restriction or fasting [38]–[42]. However, the Δ15N variations reported have sometimes been conflicting and difficult to interpret because of differences in the experimental conditions and the presence of confounding factors, such as concomitant changes in both the quality and quantity of P intake or the heterogeneous extent of body mass depletion in the event of starvation. In all cases, the underlying mechanisms proposed for associated isotopic variations remain speculative. Using the calibrated model, we simulated several scenarios consisting in different N flux modifications that might result from moderate (e. g. variation in P quality; Simulation #3) or severe (e. g. fasting conditions; Simulation #4) nutritional modulations.
Our experimental data, as well as the model predictions for tissue Δ15N values, are consistent with the few findings available in the literature on rodents [13], [33], [34], [48]–[52] (Table S5). In contrast, whereas in the literature urine is usually reported to be 15N-depleted compared to the diet [13]–[15], [53]–[59], in this study we observed and simulated Δ15N values in urine that did not differ significantly from zero. Given that urinary N accounts for the largest part of N losses, the isotopic composition of N losses was globally similar to that of N intake (i. e. , null Δ15N in N losses). As a result, amounts of 15N intake and losses were equal, which ensured that the body was globally at isotopic steady state at the time of the measurements. A negative Δ15N in N losses has often been put forward to explain the trophic shift (i. e. , positive Δ15N in body tissues), but this would mean a permanent and endless Δ15N increase in adult body tissues. Because no net gain of 15N or net loss of 14N occurs in an adult animal at isotopic steady state, the trophic shift probably result from 15N accumulation during the growth period before adulthood, when animals are in positive N balance. Since N deposited in tissues is 15N-enriched relative to excreted N, net gain of N in tissues during growth should be associated with preferential 15N bioaccumulation. In addition, the higher 15N abundance observed in P than in AA fractions of tissues is a novel and important finding that had not previously been reported, because tissues are usually analysed as a whole, without making a distinction between the AA and P fractions. It should be noted that some other N-containing compounds like nucleotides in tissues, creatine in muscle and uric acid in plasma are probably present, but in relatively small concentrations, in what we considered as the “free AA” fractions, since there is no way to separate these minor compounds from the main free AA. However, based on their known relative concentrations, these potential contaminants may not alter significantly our estimates of the free AA isotopic values. As suggested by in vitro studies [9], [60]–[64], our model confirmed that some metabolic pathways are associated with isotope effects that explain the well-known, but still poorly understood, δ15N trophic step, and also the observed, but also misunderstood, Δ15N differences between and within tissues. Whereas there are very few quantitative data in the literature concerning the amplitude of the isotope fractionation associated with metabolic pathways in vivo, one highlight of our work is that it enabled the localisation and quantification of these isotope effects (Table S3, ε values). Globally, according to our model, no net isotope effects were likely to be associated with pathways involving simple N transport without metabolic transformation (e. g. , AA exchange fluxes between plasma and tissues, N transfers throughout the gastrointestinal tract and N losses via the faeces, hair and desquamation, etc.), inasmuch as these isotope effects were not required to reproduce intra-individual Δ15N variations. Conversely, we identified the existence of isotope effects associated with the tissue AA oxidation and urea production pathways. These predictions are in line with the fact that the most likely processes inducing isotopic fractionation are those which involve enzymatic transfers of amino groups such as deamination and transamination reactions [9], [11], [17]. However, although we were able to confirm the existence of an isotope effect associated with deamination, we showed that this mechanism alone could clearly not explain the δ15N trophic shift in tissues. Simple whole-body models, which do not distinguish between different body tissues and consider all body P as a single compartment, conclude that the isotopic fractionation associated with AA catabolism and N elimination is sufficient to explain 15N accumulation in tissues [11], [12], [16], [65]. But thanks to the multi-tissue representation of our model, we were able to demonstrate that isotope effects with distinctive amplitudes are necessarily involved in several metabolic pathways to give rise to the Δ15N variations observed among tissues and among N fractions within tissues. More specifically, we predicted varied non-null ε values associated with urea production and urinary excretion fluxes, as well as with P synthesis fluxes in tissues and some intestinal secretion and reabsorption fluxes. It has to be noted that the non-null ε factors in our model do not strictly represent isotope effects associated to specific individual chemical processes, but rather aggregate several fractionation processes that may occur on different pathways. Generally speaking, non-null ε factors can reflect the heterogeneity of the precursor pool and the fact that only a subset of the compounds in the precursor pool (some specific AA for instance), with a distinct δ15N, is used in a given pathway. In the case of exchanges that involve opposite reactions (e. g. , intestinal absorption and secretion, or P synthesis and breakdown), non-null ε factors usually represent the net fractionation effect associated with the bidirectional exchange (see Text S1 for a further discussion on the physiological plausibility of our estimated ε values). To avoid over-parameterization of the model, we also chose to represent all its N fluxes by linear, first-order dynamics. Indeed, on grounds of parsimony, it was not necessary to make the model equations nonlinear and more complex (e. g. , using saturable transfer laws), since the modeling results were judged satisfactory with regard to the general knowledge on the functioning of the N metabolic system and our experimental data. From the model simulations, we observed that nutritionally-induced variations in P turnover and AA catabolism fluxes led to Δ15N changes that could be of varied extents and durations (transient or permanent). Moreover, when comparing the initial and final steady states, we found that changes in the sizes and Δ15N of body N pools were not systematically coupled: compartment sizes may differ between two steady states without associated differences in Δ15N values, and vice versa. For instance, in the case of net P accretion or depletion, Δ15N values were similar at the initial and final steady states while the elemental steady states were different. Δ15N were only temporary changed during the period necessary to achieve the new steady state (i. e. , when the P synthesis and degradation fluxes were unbalanced). More specifically, a permanent P accretion (or inversely, depletion) was associated with a transient decrease (or increase) in the tissue P Δ15N value (Figures 3A–D). Because, according to our predictions, the isotopic variations induced by alterations to P synthesis and degradation fluxes are only transient and of small amplitude, they will probably be very difficult to detect in practice. Therefore such fluxes modulations are neither sufficient nor necessary to explain Δ15N differences that can be observed between two distinct metabolic states. The fact that tissue Δ15N values do not record the entire history of P anabolic and catabolic phases that specifically result from transient imbalances between P synthesis and degradation probably limits the degree of both the inter- and intra-individual Δ15N variability in a given tissue. In contrast to the limited impact of modulations in bidirectional fluxes, variations in fluxes involved in metabolic branched pathways, such as modifications of the relative orientation of tissue AA toward their different metabolic and transfer pathways, are predicted to lead to lasting changes in Δ15N values (i. e. , with distinct final and initial isotopic steady states), even without a concomitant variation in the elemental steady state (Figures 3E–H). Generally speaking, based on both our analytical (model equations, in Table S1) and numerical (model simulations, Figures 3–4) results, we can predict that the steady-state isotopic signature of a given tissue should depend both directly on its own metabolism and indirectly on the isotopic signatures of other tissues. Indeed, in peripheral tissues such as muscle, variations in metabolism would directly affect tissue AA and P Δ15N values through changes in the proportion of AA catabolism relative to net P synthesis and outward transport (as represented by the integrated index p). Besides, because of blood-tissue AA exchanges, a metabolic-induced Δ15N change in a given tissue could modify plasma Δ15N values, and thus also secondarily those of other body tissues without the metabolism of the latter necessarily being changed. In splanchnic tissues, which have a more central and connected position in the metabolic network, steady-state Δ15N values are predicted to be affected by local tissue metabolism and also by the relative contribution of other metabolic pathways, such as urea recycling and salvage by N intestinal reabsorption (see Table S1 for detailed equations). Furthermore, when simulating Δ15N trajectories in different body tissues after a metabolic change specifically affecting splanchnic tissues, we predicted that Δ15N values varied in all tissues but according to different kinetics, the new isotopic steady states being reached at different speeds (Figure S3). These results are of particular interest in terms of determining the time required for such metabolic alterations to become isotopically detectable, as a function of the tissue under study. This kinetic characteristic also paves the way to using isotopic measurements obtained concomitantly in several tissues to estimate the timing of a metabolic alteration, or, when used in ecological studies, to infer the time of a diet-shift or migratory movement in the context of dietary δ15N changes [17], [66]. For such ecological applications, the predicted half-lives and times needed for tissues to reach their isotopic steady state after a dietary and/or a metabolic change are of considerable value (Table S4). We also used the model to investigate how tissue Δ15N values might vary under particular changes to dietary P intake and which N flux alterations would most likely be responsible for reported Δ15N variations under such conditions. Globally, under moderate qualitative or quantitative changes in the dietary P intake, which preserve the whole body N balance, our simulations suggested that Δ15N would be minimal when the P intake optimally matched AA demand and N requirements. Indeed, we predicted higher tissue Δ15N values when the N intake deviated qualitatively or quantitatively from an optimum intake. This results from the fact that an altered dietary P quality, due to a less well-balanced AA composition, or an excessive P intake, leads to a greater relative orientation of tissue AA to the catabolic vs. anabolic pathways, and consequently to less efficient tissue P synthesis and dietary P anabolic use. These predictions agree with the results of studies performed in several different species that have shown that Δ15N decreased as the dietary P biological value or anabolic use efficiency increased [36], [67], [68]. More specifically, we simulated different N flux modulation scenarios that could result from a change to dietary P quality. These scenarios consisted mainly in increasing tissue AA oxidation fluxes (i. e. , decreasing tissue P synthesis efficiency) in different tissues and to various extents. The model predicts that an increase in AA catabolism in splanchnic or peripheral tissues would result in contrasting Δ15N changes (increase or decrease, respectively) in the tissue P. Since published studies have mostly reported higher Δ15N values in tissues with dietary P of poorer quality [8], [13], [33], [34], [68], our simulation results suggest that the higher urea production induced by a P of poorer quality is caused by an increase in the oxidation of splanchnic rather than peripheral AA. The model also predicts that the amplitude of increase in tissue Δ15N values after an increase in splanchnic AA oxidation would be all the larger as the resulting increase in urea production is not entirely balanced by an increase in urea recycling but, rather, associated with an alteration to peripheral metabolism (Figure 4). However, tissue Δ15N values would probably not constitute specific markers of the relative changes in urea recycling and whole peripheral metabolism, because similar Δ15N values could be obtained with distinct sets of urea recycling efficiency and peripheral AA delivery values. In addition, the Δ15N variations predicted in urinary and body urea result from numerous and possibly opposing isotopic variations in the splanchnic and peripheral tissues, so that Δ15N variations in urinary and body urea at an isotopic steady state are likely not to be informative about body N flux modulations. In contrast, faeces Δ15N might constitute an interesting marker of enterohepatic urea recycling efficiency (Figure S5). Globally, the simulations demonstrate that combined measurements of Δ15N changes in various tissues and N pools might help to disentangle the underlying intricate metabolic modulations associated with changes in dietary P quality. Our model also provides insight into isotopic variations under more stringent nutritional alterations to metabolic fluxes, such as during nutritional stress, fasting or starvation. Although muscle P wasting has been well described under prolonged starvation, the flux modulations involved have not been fully identified [69]. Our simulations showed that a drastically reduced or zeroed N intake could induce a global δ15N increase in most body and elimination pools. Amplitudes of the simulated δ15N variations differed between tissues and depended on the N flux modulations responsible for P mass loss (Figure 5). The predicted δ15N increases in urine and most tissues (except red blood cells) in response to starvation are in line with most literature values reported for mammals experiencing N imbalance [14], [15], [40], [42], although conflicting results have been reported across various tissues, species and conditions of nutritional restriction [5], [6], [29], [30]. For instance, some studies showed a general increase in the δ15N values of various tissues in fasting or P deprived birds [29], [30], whereas other studies reported δ15N changes in only some tissues (e. g. , in liver, SI mucosa and heart, but not in muscle, in fasting Arctic ground squirrels during hibernation [40]) or no δ15N changes in tissues despite an increase in excreta δ15N [45], [46]. Our model was able to reproduce qualitatively such different results and our simulations suggest that δ15N variations under starvation may be different depending on how and which metabolic fluxes are altered. We indeed found that a muscle mass loss that is mainly induced by an increase in the muscle P degradation, should lead to a similar increase in the δ15N values in all body tissues, which is consistent with reports on quails under starvation [39]. By contrast, when muscle mass loss is caused by an alteration to muscle P synthesis, together with an enhanced oxidative utilization of muscle AA, we predicted a greater δ15N increase in splanchnic tissues and almost no δ15N variations in muscle, as has been reported in hibernating squirrels [40]. A global body δ15N increase in response to starvation had also been predicted by previous simple models in the literature [11], [16], which considered all body P as a unique and homogeneous compartment. In such models, urinary N excretion was considered as the only fractionating process, with a preference for 14N, so that δ15N in the single-body P pool systematically increased in line with an increase in the proportion of excreted vs. ingested N. Unlike these over-simplified models, our multi-tissue model takes into account possible isotopic fractionating processes associated with between and within tissue N fluxes, thus explaining our seemingly divergent experimental results. Indeed, our model can explain the tissue-specific δ15N variations reported during starvation as resulting not only from enhanced urinary excretion but also possibly from a tissue-specific increase in endogenous P degradation, leading to an enhanced re-use of 15N-enriched AA for P synthesis after their inter-organ redistribution. More generally, in situations of N imbalance, our model is able to discriminate between variations in the P synthesis and breakdown fluxes leading to similar P mass variations. Our simulation results thus demonstrate the physiological plausibility of the model we have developed. The behaviour of the model is in line with general knowledge on the functioning of the N metabolic system and with the fragmented data that have been reported on nutritionally-induced Δ15N variations. Thanks to its multi-tissue representation, our model provides a detailed and integrated insight into the partitioning of different N metabolic fluxes between and within tissues, and a clearer understanding of the metabolic processes that generate isotopic fractionation and their interactions. We have shown that, contrary to the common hypothesis, urea production is not the only process responsible for the well-known, but poorly-understood, δ15N trophic shift (i. e. , positive Δ15N values in tissues). Numerous other metabolic processes, such as P synthesis, AA intracellular metabolism, intestinal absorption and urea recycling through the colon, are actually likely involved in the accumulation of 15N in tissues. Interestingly, our findings suggest that tissue Δ15N could be used as an indicator of how well the diet matches the metabolic demand for AA in tissues. The existence of such a natural isotopic biomarker paves the way towards better assessing the notion of dietary P quality under various physiopathological conditions. In addition, using model simulations, we were able to highlight that tissue Δ 15N values are closely related to the distribution of N fluxes within the body, and that Δ15N measurements could therefore be used as biomarkers for the metabolic impact of nutritional conditions. Although in a given tissue Δ15N often seems to be a sensitive but rarely specific marker of particular dietary and metabolic conditions, we showed that simultaneous measurements of Δ15N in various tissues can be used to characterize a particular metabolic state. Accordingly, the present study thus constitutes proof of concept that natural N isotope abundances are interpretable biomarkers for the metabolic impact of nutritional conditions. A multi-tissue mechanistic modeling approach, such as that developed during this study, in order to understand the mechanisms underlying natural isotopic signatures, is a prerequisite for further research on their use in nutrition and physiopathology, in addition to their usual applications in ecology and anthropology [5]. Isotopic signatures at natural levels of abundance therefore appear to constitute a novel and promising tool to investigate how various N fluxes may be reorganised or altered in a coordinated manner to adapt to specific nutritional or physiopathological conditions. They offer interesting applications for the simple and early detection of such conditions and evaluation of the impact of nutritional strategies.
Body proteins ensure vital functions, and their constancy is maintained through the tight coordination of many nitrogen metabolic fluxes, but our understanding of how this flux system is regulated, and sometimes dysregulated, remains fragmentary and incomplete. Besides, body tissues are generally naturally enriched in the heavier stable nitrogen isotope (15N) over the diet: this 15N bioaccumulation (Δ15N) varies depending on tissues and metabolic orientations, likely as the result of isotope effects associated to some metabolic pathways. We used a novel approach, combining multi-tissue Δ15N measurements and their analysis using modeling, to understand how body Δ15N values relate to nitrogen fluxes. The multi-tissue model we have developed provides a clearer understanding of the metabolic processes that generate isotopic fractionation, and of how tissue Δ15N values are modulated in response to changes in the body distribution of specific nitrogen fluxes. We show that Δ15N values tend to rise when the amino acids intake does not optimally fit the metabolic demand, and that Δ15N values constitute natural and interpretable signatures of nutritionally-induced variations in nitrogen fluxes. This approach constitutes a new promising tool to investigate how nitrogen metabolism is nutritionally or physiopathologically reorganized or altered, and promises interesting applications in many areas (nutrition, pathology, ecology, paleontology, etc).
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Natural Isotopic Signatures of Variations in Body Nitrogen Fluxes: A Compartmental Model Analysis