In Silico Analysis Identifies Intestinal Transit as a Key Determinant of Systemic Bile Acid Metabolism

Bile acids fulfill a variety of metabolic functions including regulation of glucose and lipid metabolism. Since changes of bile acid metabolism accompany obesity, Type 2 Diabetes Mellitus and bariatric surgery, there is great interest in their role in metabolic health. Here, we developed a mathematical model of systemic bile acid metabolism, and subsequently performed in silico analyses to gain quantitative insight into the factors determining plasma bile acid measurements. Intestinal transit was found to have a surprisingly central role in plasma bile acid appearance, as was evidenced by both the necessity of detailed intestinal transit functions for a physiological description of bile acid metabolism as well as the importance of the intestinal transit parameters in determining plasma measurements. The central role of intestinal transit is further highlighted by the dependency of the early phase of the dynamic response of plasma bile acids after a meal to intestinal propulsion

Model-based data analysis of individual human postprandial plasma bile acid responses indicates a major role for the gallbladder and intestine

BACKGROUND: Bile acids are multifaceted metabolic compounds that signal to cholesterol, glucose, and lipid homeostasis via receptors like the Farnesoid X Receptor (FXR) and transmembrane Takeda G protein-coupled receptor 5 (TGR5). The postprandial increase in plasma bile acid concentrations is therefore a potential metabolic signal. However, this postprandial response has a high interindividual variability. Such variability may affect bile acid receptor activation. METHODS: In this study, we analyzed the inter- and intraindividual variability of fasting and postprandial bile acid concentrations during three identical meals on separate days in eight healthy lean male subjects using a statistical and mathematical approach. MAIN FINDINGS: The postprandial bile acid responses exhibited large interindividual and intraindividual variability. The individual mathematical models, which represent the enterohepatic circulation of bile acids in each subject, suggest that interindividual variability results from quantitative and qualitative differences of distal active uptake, colon transit, and microbial bile acid transformation. Conversely, intraindividual variations in gallbladder kinetics can explain intraindividual differences in the postprandial responses. CONCLUSIONS: We conclude that there is considerable inter- and intraindividual variation in postprandial plasma bile acid levels. The presented personalized approach is a promising tool to identify unique characteristics of underlying physiological processes and can be applied to investigate bile acid metabolism in pathophysiological conditions

Altered bile acid kinetics contribute to postprandial hypoglycaemia after Roux-en-Y gastric bypass surgery

Background/objectives: Bile acids (BA) act as detergents in intestinal fat absorption and as modulators of metabolic processes via activation of receptors such as FXR and TGR5. Elevated plasma BA as well as increased intestinal BA signalling to promote GLP-1 release have been implicated in beneficial health effects of Roux-en-Y gastric bypass surgery (RYGB). Whether BA also contribute to the postprandial hypoglycaemia that is frequently observed post-RYGB is unknown. Methods: Plasma BA, fibroblast growth factor 19 (FGF19), 7α-hydroxy-4-cholesten-3-one (C4), GLP-1, insulin and glucose levels were determined during 3.5 h mixed-meal tolerance tests (MMTT) in subjects after RYGB, either with (RYGB, n = 11) or without a functioning gallbladder due to cholecystectomy (RYGB-CC, n = 11). Basal values were compared to those of age, BMI and sex-matched obese controls without RYGB (n = 22). Results: Fasting BA as well as FGF19 levels were elevated in RYGB and RYGB-CC subjects compared to non-bariatric controls, without significant differences between RYGB and RYGB-CC. Postprandial hypoglycaemia was observed in 8/11 RYGB-CC and only in 3/11 RYGB. Subjects who developed hypoglycaemia showed higher postprandial BA levels coinciding with augmented GLP-1 and insulin responses during the MMTT. The nadir of plasma glucose concentrations after meals showed a negative relationship with postprandial BA peaks. Plasma C4 was lower during MMTT in subjects experiencing hypoglycaemia, indicating lower hepatic BA synthesis. Computer simulations revealed that altered intestinal transit underlies the occurrence of exaggerated postprandial BA responses in hypoglycaemic subjects. Conclusion: Altered BA kinetics upon ingestion of a meal, as frequently observed in RYGB-CC subjects, appear to contribute to postprandial hypoglycaemia by stimulating intestinal GLP-1 release

Model-Based Quantification of the Systemic Interplay between Glucose and Fatty Acids in the Postprandial State

In metabolic diseases such as Type 2 Diabetes and Non-Alcoholic Fatty Liver Disease, the systemic regulation of postprandial metabolite concentrations is disturbed. To understand this dysregulation, a quantitative and temporal understanding of systemic postprandial metabolite handling is needed. Of particular interest is the intertwined regulation of glucose and non-esterified fatty acids (NEFA), due to the association between disturbed NEFA metabolism and insulin resistance. However, postprandial glucose metabolism is characterized by a dynamic interplay of simultaneously responding regulatory mechanisms, which have proven difficult to measure directly. Therefore, we propose a mathematical modelling approach to untangle the systemic interplay between glucose and NEFA in the postprandial period. The developed model integrates data of both the perturbation of glucose metabolism by NEFA as measured under clamp conditions, and postprandial time-series of glucose, insulin, and NEFA. The model can describe independent data not used for fitting, and perturbations of NEFA metabolism result in an increased insulin, but not glucose, response, demonstrating that glucose homeostasis is maintained. Finally, the model is used to show that NEFA may mediate up to 30-45% of the postprandial increase in insulin-dependent glucose uptake at two hours after a glucose meal. In conclusion, the presented model can quantify the systemic interactions of glucose and NEFA in the postprandial state, and may therefore provide a new method to evaluate the disturbance of this interplay in metabolic disease.Funding Agencies|European Union [305707]; Linkoping Initiative within Life Science Technologies; Ostergotland County Council; Swedish Research Council; AstraZeneca</p

Model calibration on meal responses.

<p>Data from D_MEAL<b>[<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0135665#pone.0135665.ref048" target="_blank">48</a>]</b> (red errorbars) and model simulations (S_ext, grey curves; and S_sel, green shading as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0135665#pone.0135665.g004" target="_blank">Fig 4</a>) in response to an OGTT (A) and an OFTT (B).</p

Model calibration on clamp data.

<p>To investigate propagation of parameter uncertainty in predictions and analyses a collection of parameter sets was selected. Measurements from D_CLAMP1 (hyperinsulinemic, euglycemic clamp) <b>[<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0135665#pone.0135665.ref032" target="_blank">32</a>]</b> (A,B, red errorbars) and D_CLAMP2<b>[<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0135665#pone.0135665.ref033" target="_blank">33</a>]</b> (C,D, red errorbars) with superimposed model outputs as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0135665#pone.0135665.g003" target="_blank">Fig 3</a>. Here, simulations representing the complete collection of selected parameter sets (S_sel) are shown, depictured as dots shaded from dark green for poor fits of EGP (high values of V_EGP) to light green for low V_EGP. We note in C, that not all parameter sets from S_sel describe the data, and that a bad correspondence of the simulations in A and C is shown with dark green color.</p

<i>In silico</i> cholesterol FPLC profiles of transgenic mice.

<p><b>A.</b> Simulated cholesterol profile of the SR-B1 knock-out transgenic mouse. To simulate the SR-B1 knock-out mouse, the selective uptake parameter was set to between 10⁻¹% (black) and 5% (blue) of the original value. For comparison, the untreated C57Bl/6J mouse cholesterol profile is drawn in red. For comparison, we refer to the FPLC profile of SR-B1 deficient mice in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003579#pcbi.1003579-Rigotti1" target="_blank">[30]</a>. <b>B.</b> Change in plasma cholesterol concentration for the <i>in silico</i> transgenic mice as depicted in A, C and E. <b>C.</b> Simulated cholesterol profile of the PLTP knock-out transgenic mouse. To simulate the PLTP knock-out mouse, the parameter was diminished to values between 30% (black) and 50% (light blue) of its original value, increasing in steps of 5. The untreated C57Bl/6J mouse profile is again shown in red. Note that because the perturbed parameter in this case cannot be presumed to be solely dependent on PLTP activity, the parameter value was not reduced below 30%. For comparison, we refer to the FPLC profile of PLTP deficient mice in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003579#pcbi.1003579-Jiang1" target="_blank">[37]</a>. <b>D.</b> Change in plasma triglyceride concentration for the in silico transgenic mice as depicted in A, C and E. <b>E.</b> Simulated cholesterol profile of the LDLr knock-out mouse. To simulate the LDLr knock-out mouse, both VLDL sub-model whole-uptake parameters and were diminished to a factor between 40% and 92.5% of their wild-type value. For comparison, we refer to the FPLC profile of LDLr deficient mice in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003579#pcbi.1003579-Ishibashi3" target="_blank">[43]</a>. The visualized <i>in silico</i> profiles have all been generated with parameter set X1. For clarity, the highest measured fractions of the FPLC profile have not been pictured. Further quantitative analysis of the results as well as the corresponding in silico profiles generated with X2 are presented in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003579#pcbi.1003579.s005" target="_blank">Text S5</a>.</p

<i>In silico</i> fast protein liquid chromatography profiles of untreated C57Bl/6J mice.

<p><b>A.</b> Cholesterol FPLC profile of untreated mice, experimental data and simulated profile. FPLC profile (black) of pooled plasma of moderately fasted, untreated C57Bl/6J mice and simulated FPLC profile (blue) total cholesterol content. The <i>in silico</i> profile was calculated with an optimized parameter set following model parametrisation (<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003579#pcbi.1003579.s004" target="_blank">Text S4</a>, parameter set X1). <b>B.</b> TG FPLC profile of untreated mice, experimental data and simulated profile. Experimental (black) and simulated (blue) TG profiles as in A. <b>C.</b> Computed profile of HDL. In addition to calculation of the measured profiles (A and B), calculation of profiles of all included model components is facilitated by the model. The parameters used to generate this profile are provided in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003579#pcbi.1003579.s004" target="_blank">Text S4</a>. <b>D.</b> Computed PL profile, computed with the parameters provided in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003579#pcbi.1003579.s004" target="_blank">Text S4</a>. For clarity, the highest measured fractions of the FPLC profile have not been pictured.</p

Overview of extended HDL metabolism.

<p>HDL metabolism, as depicted in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003579#pcbi-1003579-g002" target="_blank">Figure 2B</a>, with extensions. For clarity, the original model of HDL metabolism is shown in grey-scale, while the three extensions are shown in red, blue and green respectively. The additional cholesterol accumulation of E1 is depicted in red. The additional lipoprotein uptake modelled in E2 is shown in blue. Finally the extension to include additional large nascent HDL as described by E3 is included via the green arrow. Note that while all extensions are shown here in the same figure, the three extensions are included in the model separately. More details on the model extensions are provided in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003579#pcbi.1003579.s006" target="_blank">Text S6</a>.</p

Clamp datasets: experimental data and simulation results.

<p>Measurements from D_CLAMP1 (hyperinsulinemic, euglycemic clamp) <b>[<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0135665#pone.0135665.ref032" target="_blank">32</a>]</b> (A,B, red errorbars) and D_CLAMP2<b>[<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0135665#pone.0135665.ref033" target="_blank">33</a>]</b> (C,D, red errorbars) with superimposed model outputs. The simulation represents the parameter set in S_sel that corresponds to a minimal value for V_EGP. A. Mean EGP as measured over the final half of a 360 minute clamp with low, medium and high NEFA concentration. B. Total glucose uptake (conditions and measurement time as in A). C. EGP measured during the final 60 minutes of the 120 minute clamp in experiments of group C that underwent an eu-insulinemic, hyperglycemic clamp with a saline infusion (C-) and with a combined intralipid and heparin infusion (C+). D. Total glucose uptake as in C, for experiments with a hyperinsulinemic euglycemic clamp (group A-, A+), hyperinsulinemic, hyperglycemic clamp (group B-, B+), and an eu-insulinemic, hyperglycemic clamp (group C- and C+). A short summary of the implementation in the model is provided in the Materials and Methods; full details of implementation can be found in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0135665#pone.0135665.s002" target="_blank">S1 File</a>.</p