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Supplementary Text - Overview of nutrition for endurance athletes The body of an athlete converts large amounts of energy. Therefore, a comprehensive model of energy conversion and metabolism in the athlete should in the future also include nutrition intake and absorption. Some relevant and interesting aspects to take into consideration when modeling nutrition for athletes are discussed in this section. With regard to nutrition intake, recent reviews on nutrition composition before, during and after exercise [1] and on the timing of nutrition in relation to exercise [2] offer the following general recommendations. Energy and macronutrient needs, especially carbohydrate and protein, must be met during times of high physical activity to maintain body weight, replenish glycogen stores, and provide sufficient protein to build and repair tissue. Fat intake should be sufficient to provide the essential fatty acids and fat-soluble vitamins while also contributing energy for weight maintenance. Especially during exercise a steady supply of carbohydrates to replenish muscle glycogen stores and to support energy metabolism is important. In line with the importance of carbohydrates supply, the influence of glycaemic index (GI), a measure to functionally rank carbohydrates based on their actual postprandial blood glucose response, on exercise performance has received much attention. However, although at the biochemical level there has been consistent evidence that altering the GI may change fat and carbohydrate oxidation during exercise [3], there is no consensus on whether consuming carbohydrates of differing GI improves endurance performance [4]. It has been suggested that the glycaemic load (GL), a relatively novel concept in the area of sports nutrition which considers the overall glycaemic effect of a diet and not the amount of carbohydrate alone, may be a better predictor of glycaemic responses than the GI alone [5]. While carbohydrates obviously are important, of special interest is that different nutrients have been found to interact in their effectivity to achieve a prime main target i.e. inproving 1 glycogen storage which is the major endurance-limiting substrate. Thus, Ivy et al. [6] found that the addition of protein (Pro) to a carbohydrate (CHO) supplement enhanced aerobic endurance performance above that which occurred with CHO alone. Studies of Zawadzki et al. [7] and Ivy et al. [8] comparing CHO, Pro, and CHO-Pro supplements suggest that a CHOPro supplement is more effective for the rapid replenishment of muscle glycogen after exercise than a CHO supplement of equal CHO or caloric content, or than a supplement of Pro alone. This advantage seems to hold even when the caloric content of the CHO-Pro mixture is lower than that of the CHO mixture alone, demonstrating the presence of a true interaction effect [9]. Adding Pro to a low-calorie CHO sports drink may be an effective strategy to enhance aerobic capacity while limiting carbohydrate and caloric consumption [10]. The CHO-Pro interaction effect is probably a result of the interaction of CHO and Pro on insulin secretion [7]. Van Loon et al. [11] found that a mixture drink of wheat protein hydrolysate, free leucine, and phenylalanine, appeared to generate the maximal insulinotropic effect when co-ingested with carbohydrate. Further contributing to this issue, Miller et al. [12] examined the metabolic response of hormones (insulin, glucagon, epinephrine, norepinephrine, growth hormone, testosterone, and cortisol) and metabolites (glucose, lactate, free fatty acids, and selected amino acids) in plasma to a provision of mixed proteincarbohydrate supplementation during endurance exercise. Alternatively combining CHO with fat in interventions aiming to increase fat availability before exercise has been shown to reduce carbohydrate utilization during exercise, but does not appear to result in ergogenic benefits [13]. Data from these and future nutrition studies focusing on GI, GL, carbohydrates, proteins, fat as well as their interactions, can be used to construct and calibrate relevant sections of a computer model focusing on the influence of nutrion in the athlete. Since the gastrointestinal tract is the site of nutrient absorption, it is a key organ also for the athlete. In an excellent review, Gisolfi [14] demonstrates that the gut can meet the 2 demands of prolonged severe exercise and can even show signs of adaptation to exercise training but can also signal conditions of impending injury. During severe exercise, splanchnic blood flow is markedly reduced and intestinal permeability can increase by opening tight junctions, which is considered the central mechanism initiating immunologic and inflammatory events that can severely impair gut structure and function. In a recent review by de Oliveira et al., gut ischemia was confirmed as the probable main cause of nausea, vomiting, abdominal pain and (bloody) diarrhea experienced by athletes [15]. Both reviews in fact underline the importance of nutrition that supports gastrointestinal health and integrity. Therefore, it is relevant to also consider the possible beneficial role of gastrointestinal microbial metabolism in relation to sports nutrition. Not only does gut bacterial metabolism contribute significantly to the body’s energy balance [16], but one of the metabolic products, the short-chain fatty acid (SCFA) butyrate, is a key gut healthpromoting compound [17,18]. The absolute and relative gut bacterial synthesis rates of butyrate and the other SCFA acetate (a precursor of the important energy substrate acetylCoA) and propionate (a precursor of hepatic gluconeogenesis) depend on the gut microbial flora composition and can also be influenced by dietary interventions (i.e., prebiotics) [19-21]. The link between nutritional composition and desired gut bacterial SCFA production, is therefore necessarily complex, requiring a selective measurement of microbial metabolites and a quantification of the involved metabolic fluxes in connection with analysis of the gut microbiota composition. Mathematical modeling is needed to interpret these data. However, the human gut microbiota consists of hundreds of microbial species. To study metabolism in this complex situation a workable alternative is an in vitro model containing the microbiota. To illustrate the combination of measurements in an in vitro model with analysis of measured data by mathematical models, we briefly discuss our recent study of the conversion of starch into the main short chain fatty acids (SCFA) in human gut microbiota using isotope 3 labeling experiments. RNA-Stable Isotope Probing (SIP) analysis has identified the main bacterial species involved in [U-13C] starch fermentation [22]. To determine microbial metabolites and SCFA production, we used Nuclear Magnetic Resonance (NMR) analyses [23] in combination with a computational modeling approach for metabolite and flux analysis [24]. For interpretation of the data, a metabolic model (Supplementary Figure 1) was assembled from literature information [25, 26] and implemented using ordinary differential equations (ODEs). The necessary isotope labeling experiments were done using an in vitro model of the human gut (TIM-2) [27, 28], which facilitated to measure the time course of isotope incorporation in the key SCFA acetate, propionate and butyrate by mass spectrometry (MS) and NMR spectroscopy, in addition to providing the isotopically labeled biomass for RNA-SIP analyses. With the described approach, we successfully quantified multiple metabolic parameters defining the metabolic fluxes in the microbiota using global parameter optimization. The RNA SIP analyses [22] in combination with the metabolic data suggest that metabolic cross-feeding occurs in the system, where populations related to Ruminococcus bromii are the primary starch degrader, while those related to Prevotella spp., Bifidobacterium adolescentis and Eubacterium rectale might be further involved in the trophic chain, thus giving insight in nutrition-controlled, microbe-mediated gut metabolism. This approach demonstrates how an in vitro model of the biochemistry can be combined with computational analysis. Given the enormous complexity of microbial metabolism in the gut, this is a viable alternative to complete in silico modelling. Legend to Supplementary Figure 1. Carbon transition model of the gut bacteria. The parameters estimated in our study are: relative splitting factors of metabolic flow (W, X, Y and Z), labeled influx from starch (J in), 4 unlabeled influxes (JdilA and JdilB), back flux from acetate to acetyl CoA (Jxch), CO2 leaving the system (Jout) and dialysis rate constants (kpropionate, kacetate and kbutyrate). This model was used to derive metabolic fluxes from stable isotope measurement data obtained with a human microbiota in an in vitro system [24]. 5 REFERENCES 1. Rodriguez, N. R., Di Marco, N. M. & Langley, S. 2009 American College of Sports Medicine position stand. Nutrition and athletic performance. 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