Modeling SILAC Data to Assess Protein Turnover in a Cellular Model of Diabetic Nephropathy.

Protein turnover rate is finely regulated through intracellular mechanisms and signals that are still incompletely understood but that are essential for the correct function of cellular processes. Indeed, a dysfunctional proteostasis often impacts the cell's ability to remove unfolded, misfolded, degraded, non-functional, or damaged proteins. Thus, altered cellular mechanisms controlling protein turnover impinge on the pathophysiology of many diseases, making the study of protein synthesis and degradation rates an important step for a more comprehensive understanding of these pathologies. In this manuscript, we describe the application of a dynamic-SILAC approach to study the turnover rate and the abundance of proteins in a cellular model of diabetic nephropathy. We estimated protein half-lives and relative abundance for thousands of proteins, several of which are characterized by either an altered turnover rate or altered abundance between diabetic nephropathic subjects and diabetic controls. Many of these proteins were previously shown to be related to diabetic complications and represent therefore, possible biomarkers or therapeutic targets. Beside the aspects strictly related to the pathological condition, our data also represent a consistent compendium of protein half-lives in human fibroblasts and a rich source of important information related to basic cell biology.

Physiologically based modelling of the antiplatelet effect of aspirin: A tool to characterize drug responsiveness and inform precision dosing.

A computational approach involving mathematical modeling and in silico experiments was used to characterize the determinants of extent and duration of platelet cyclooxygenase (COX)-1 inhibition by aspirin and design precision dosing in patients with accelerated platelet turnover or reduced drug bioavailability. To this purpose, a recently developed physiologically-based pharmacokinetics (PK) and pharmacodynamics (PD) model of low-dose aspirin in regenerating platelets and megakaryocytes, was used to predict the main features and determinants of platelet COX-1 inhibition. The response to different aspirin regimens in healthy subjects and in pathological conditions associated with alterations in aspirin PK (i.e., severely obese subjects) or PD (i.e., essential thrombocytemya patients), were simulated. A model sensitivity analysis was performed to identify the main processes influencing COX-1 dynamics. In silico experiments and sensitivity analyses indicated a major role for megakaryocytes and platelet turnover in determining the extent and duration of COX-1 inhibition by once-daily, low-dose aspirin. They also showed the superiority of reducing the dosing interval vs increasing the once-daily dose in conditions of increased platelet turnover, while suggested specific dose adjustments in conditions of possible reduction in drug bioavailability. In conclusion, the consistency of our model-based findings with experimental data from studies in healthy subjects and patients with essential thrombocythemia supports the potential of our approach for describing the determinants of platelet inhibition by aspirin and informing precision dosing which may guide personalized antithrombotic therapy in different patient populations, especially in those under-represented in clinical trials or in those associated with poor feasibility.

Reduced Effective Connectivity in the Motor Cortex in Parkinson's Disease.

Fast rhythms excess is a hallmark of Parkinson's Disease (PD). To implement innovative, non-pharmacological, neurostimulation interventions to restore cortical-cortical interactions, we need to understand the neurophysiological mechanisms underlying these phenomena. Here, we investigated effective connectivity on source-level resting-state electroencephalography (EEG) signals in 15 PD participants and 10 healthy controls. First, we fitted multivariate auto-regressive models to the EEG source waveforms. Second, we estimated causal connections using Granger Causality, which provide information on connections' strength and directionality. Lastly, we sought significant differences connectivity patterns between the two populations characterizing the network graph features-i.e., global efficiency and node strength. Causal brain networks in PD show overall poorer and weaker connections compared to controls quantified as a reduction of global efficiency. Motor areas appear almost isolated, with a strongly impoverished information flow particularly from parietal and occipital cortices. This striking isolation of motor areas may reflect an impaired sensory-motor integration in PD. The identification of defective nodes/edges in PD network may be a biomarker of disease and a potential target for future interventional trials.

Corrigendum to "Genetic perturbation of IFN-α transcriptional modulators in human endothelial cells uncovers pivotal regulators of angiogenesis" [Comput Struct Biotechnol J. 2020 Dec 2;18:3977-3986. doi: https://doi.org//10.1016/j.csbj.2020.11.048. eCollection 2020].

[This corrects the article DOI: 10.1016/j.csbj.2020.11.048.].

Genetic perturbation of IFN-α transcriptional modulators in human endothelial cells uncovers pivotal regulators of angiogenesis.

Interferon-α (IFN-α) comprises a family of 13 cytokines involved in the modulation of antiviral, immune, and anticancer responses by orchestrating a complex transcriptional network. The activation of IFN-α signaling pathway in endothelial cells results in decreased proliferation and migration, ultimately leading to suppression of angiogenesis. In this study, we knocked-down the expression of seven established or candidate modulators of IFN-α response in endothelial cells to reconstruct a gene regulatory network and to investigate the antiangiogenic activity of IFN-α. This genetic perturbation approach, along with the analysis of interferon-induced gene expression dynamics, highlighted a complex and highly interconnected network, in which the angiostatic chemokine C-X-C Motif Chemokine Ligand 10 (CXCL10) was a central node targeted by multiple modulators. IFN-α-induced secretion of CXCL10 protein by endothelial cells was blunted by the silencing of Signal Transducer and Activator of Transcription 1 (STAT1) and of Interferon Regulatory Factor 1 (IRF1) and it was exacerbated by the silencing of Ubiquitin Specific Peptidase 18 (USP18). In vitro sprouting assay, which mimics in vivo angiogenesis, confirmed STAT1 as a positive modulator and USP18 as a negative modulator of IFN-α-mediated sprouting suppression. Our data reveal an unprecedented physiological regulation of angiogenesis in endothelial cells through a tonic IFN-α signaling, whose enhancement could represent a viable strategy to suppress tumor neoangiogenesis.

Stochastic modeling of human papillomavirusearly promoter gene regulation.

High risk forms of human papillomaviruses (HPVs) promote cancerous lesions and are implicated in almost all cervical cancer. Of particular relevance to cancer progression is regulation of the early promoter that controls gene expression in the initial phases of infection and can eventually lead to pre-cancer progression. Our goal was to develop a stochastic model to investigate the control mechanisms that regulate gene expression from the HPV early promoter. Our model integrates modules that account for transcriptional, post-transcriptional, translational and post-translational regulation of E1 and E2 early genes to form a functioning gene regulatory network. Each module consists of a set of biochemical steps whose stochastic evolution is governed by a chemical Master Equation and can be simulated using the Gillespie algorithm. To investigate the role of noise in gene expression, we compared our stochastic simulations with solutions to ordinary differential equations for the mean behavior of the system that are valid under the conditions of large molecular abundances and quasi-equilibrium for fast reactions. The model produced results consistent with known HPV biology. Our simulation results suggest that stochasticity plays a pivotal role in determining the dynamics of HPV gene expression. In particular, the combination of positive and negative feedback regulation generates stochastic bursts of gene expression. Analysis of the model reveals that regulation at the promoter affects burst amplitude and frequency, whereas splicing is more specialized to regulate burst frequency. Our results also suggest that splicing enhancers are a significant source of stochasticity in pre-mRNA abundance and that the number of viruses infecting the host cell represents a third important source of stochasticity in gene expression.

Obesity is associated with impaired responsiveness to once-daily low-dose aspirin and in vivo platelet activation.

BACKGROUND: The prevalence and degree of obesity is rising worldwide, increases cardiovascular risk, modifies body composition and organ function, and potentially affects the pharmacokinetics and/or pharmacodynamics of drugs. OBJECTIVES: To investigate the pharmacodynamics of once-daily low-dose aspirin in healthy obese subjects, and to assess whether body weight (BW) and body mass index (BMI) affect the pharmacology of aspirin. PATIENTS/METHODS: Otherwise healthy, obese (BMI > 30 kg/m2 ) subjects were studied before and after 3-4 weeks of 100-mg once-daily aspirin intake. Aspirin pharmacodynamics were assessed according to serum thromboxane (TX) B2 levels measured at 4 hours, 24 hours (i.e., posologic interval) and 48 hours after the last witnessed intake; age-matched and sex-matched non-obese controls were included. A previously calibrated pharmacokinetic/pharmacodynamic in silico model of aspirin was used to fit serum TXB2 data from obese subjects. At baseline, the major urinary TXA2 and prostacyclin metabolites, urinary isoprostane and plasma inflammatory biomarkers were measured. RESULTS: In 16 obese subjects (aged 47 ± 11 years; BMI of 39.4 ± 5.1 kg/m2 ), residual serum TXB2 values between 4 and 48 hours after aspirin intake were increased 3- to 5-fold as compared with controls. At 24 hours, the residual serum TXB2 level was log-linearly associated with body size over a wide range of BMI and BW values, without any apparent threshold. The in silico model predicted that reduced aspirin bioavailability would be inversely related to body size and rescued by 200 mg of aspirin once daily or 85 mg twice daily. Baseline urinary TXA2 metabolite, isoprostane and plasma C-reactive protein levels were significantly increased in obese subjects. CONCLUSIONS: Obesity is associated with impaired aspirin responsiveness, largely because of body size. Impaired inhibition of platelet activation by conventional low-dose aspirin may affect antithrombotic efficacy.

A novel triple-tracer approach to assess postprandial protein turnover.

Insulin and nutrients have profound effects on proteome homeostasis. Currently no reliable methods are available to measure postprandial protein turnover. A triple-tracer method was developed using phenylalanine stable isotope tracers to estimate appearance rates of ingested (Ra meal) and endogenous phenylalanine and the rate of phenylalanine disposal (Rd). This was compared with the "traditional" dual-tracer method, using one (1-CM)- and two (2-CM)-compartment models. For both methods, [13C6]phenylalanine was given orally, and [15N]phenylalanine was constantly infused; the triple-tracer method added [2H5]phenylalanine, infused at rates to mimic meal [13C6]phenylalanine appearance. Additionally, incorporation of meal-derived phenylalanine into specific proteins was measured after purification by two-dimensional electrophoresis. The triple-tracer approach reduced modeling errors, allowing improved reconstruction of Ra meal with a tracer-to-tracee ratio that was more constant and better estimated Rd. The 2-CM better described phenylalanine kinetics and Rd than 1-CM. Thus, the triple-tracer approach using 2-CM is superior for measuring non-steady-state postprandial protein turnover. This novel approach also allows measurement of postprandial synthesis rates of specific plasma proteins. We offer a valid non-steady-state model to measure postprandial protein turnover and synthesis of plasma proteins that can safely be applied in adults, children, and pregnant women.

TMS-evoked long-lasting artefacts: A new adaptive algorithm for EEG signal correction.

OBJECTIVE: During EEG the discharge of TMS generates a long-lasting decay artefact (DA) that makes the analysis of TMS-evoked potentials (TEPs) difficult. Our aim was twofold: (1) to describe how the DA affects the recorded EEG and (2) to develop a new adaptive detrend algorithm (ADA) able to correct the DA. METHODS: We performed two experiments testing 50 healthy volunteers. In experiment 1, we tested the efficacy of ADA by comparing it with two commonly-used independent component analysis (ICA) algorithms. In experiment 2, we further investigated the efficiency of ADA and the impact of the DA evoked from TMS over frontal, motor and parietal areas. RESULTS: Our results demonstrated that (1) the DA affected the EEG signal in the spatiotemporal domain; (2) ADA was able to completely remove the DA without affecting the TEP waveforms; (3). ICA corrections produced significant changes in peak-to-peak TEP amplitude. CONCLUSIONS: ADA is a reliable solution for the DA correction, especially considering that (1) it does not affect physiological responses; (2) it is completely data-driven and (3) its effectiveness does not depend on the characteristics of the artefact and on the number of recording electrodes. SIGNIFICANCE: We proposed a new reliable algorithm of correction for long-lasting TMS-EEG artifacts.

A four-compartment compartmental model to assess net whole body protein breakdown using a pulse of phenylalanine and tyrosine stable isotopes in humans.

The stable isotopes of phenylalanine (Phe) and tyrosine (Tyr) are often used to study whole body protein metabolism in humans. Noncompartmental approaches give limited physiological insight in the compartmental characteristics. We therefore developed a compartmental mathematical model of Phe/Tyr metabolism to describe protein fluxes by using stable tracer dynamic data in plasma following intravenous bolus of l-[ring-13C6]Phe and l-[ring-2H4]Tyr in healthy subjects. The model consists of four compartments describing Phe/Tyr kinetics. Because the model is a priori nonidentifiable, it is quantified in terms of two uniquely identifiable submodels representing two limit case scenarios, based on known physiology. The two submodels, identified by using the software SAAM II, fit well the experimental data of all individuals and provide an unbiased overview of the metabolic pathway in terms of intervals of validity of the non-uniquely identifiable variables. The model provides estimates of the flux from Phe to Tyr [4.1 ± 1.0 µmol·kg fat-free mass (FFM)-1·h-1 (mean ± SE)] and intervals of validity of the flux and pool estimates. Our preferred submodel yielded protein breakdown flux (50.5 ± 5.2 µmol·kg FFM-1·h-1), net protein breakdown (4.1 ± 1.0 µmol·kg FFM-1·h-1), Tyr from Phe hydroxylation (~12%), hydroxylated Phe (~8%), and flux ratio of Tyr to Phe arising from protein catabolism (0.68), consistent with available literature. The other submodel suggest that the assumptions made by noncompartmental analysis are consistently underestimated. Our accurate and detailed model for estimating Phe/Tyr metabolic pathways in humans might be essential to applications in a variety of scenarios describing whole body protein synthesis and breakdown in health and disease.

A rule-based model of insulin signalling pathway.

BACKGROUND: The insulin signalling pathway (ISP) is an important biochemical pathway, which regulates some fundamental biological functions such as glucose and lipid metabolism, protein synthesis, cell proliferation, cell differentiation and apoptosis. In the last years, different mathematical models based on ordinary differential equations have been proposed in the literature to describe specific features of the ISP, thus providing a description of the behaviour of the system and its emerging properties. However, protein-protein interactions potentially generate a multiplicity of distinct chemical species, an issue referred to as "combinatorial complexity", which results in defining a high number of state variables equal to the number of possible protein modifications. This often leads to complex, error prone and difficult to handle model definitions. RESULTS: In this work, we present a comprehensive model of the ISP, which integrates three models previously available in the literature by using the rule-based modelling (RBM) approach. RBM allows for a simple description of a number of signalling pathway characteristics, such as the phosphorylation of signalling proteins at multiple sites with different effects, the simultaneous interaction of many molecules of the signalling pathways with several binding partners, and the information about subcellular localization where reactions take place. Thanks to its modularity, it also allows an easy integration of different pathways. After RBM specification, we simulated the dynamic behaviour of the ISP model and validated it using experimental data. We the examined the predicted profiles of all the active species and clustered them in four clusters according to their dynamic behaviour. Finally, we used parametric sensitivity analysis to show the role of negative feedback loops in controlling the robustness of the system. CONCLUSIONS: The presented ISP model is a powerful tool for data simulation and can be used in combination with experimental approaches to guide the experimental design. The model is available at http://sysbiobig.dei.unipd.it/ was submitted to Biomodels Database ( https://www.ebi.ac.uk/biomodels-main/ # MODEL 1604100005).

Accurate Measurement of Postprandial Glucose Turnover: Why Is It Difficult and How Can It Be Done (Relatively) Simply?

Fasting hyperglycemia occurs when an excessive rate of endogenous glucose production (EGP) is not accompanied by an adequate compensatory increase in the rate of glucose disappearance (Rd). The situation following food ingestion is more complex as the amount of glucose that reaches the circulation for disposal is a function of the systemic rate of appearance of the ingested glucose (referred to as the rate of meal appearance [Rameal]), the pattern and degree of suppression of EGP, and the rapidity of stimulation of the Rd In an effort to measure these processes, Steele et al. proposed what has come to be referred to as the dual-tracer method in which the ingested glucose is labeled with one tracer while a second tracer is infused intravenously at a constant rate. Unfortunately, subsequent studies have shown that although this approach is technically simple, the marked changes in plasma specific activity or the tracer-to-tracee ratio, if stable tracers are used, introduce a substantial error in the calculation of Rameal, EGP, and Rd, thereby leading to incorrect and at times misleading results. This Perspective discusses the causes of these so-called "nonsteady-state" errors and how they can be avoided by the use of the triple-tracer approach.

Reconstruction of gene regulatory modules from RNA silencing of IFN-α modulators: experimental set-up and inference method.

BACKGROUND: Inference of gene regulation from expression data may help to unravel regulatory mechanisms involved in complex diseases or in the action of specific drugs. A challenging task for many researchers working in the field of systems biology is to build up an experiment with a limited budget and produce a dataset suitable to reconstruct putative regulatory modules worth of biological validation. RESULTS: Here, we focus on small-scale gene expression screens and we introduce a novel experimental set-up and a customized method of analysis to make inference on regulatory modules starting from genetic perturbation data, e.g. knockdown and overexpression data. To illustrate the utility of our strategy, it was applied to produce and analyze a dataset of quantitative real-time RT-PCR data, in which interferon-α (IFN-α) transcriptional response in endothelial cells is investigated by RNA silencing of two candidate IFN-α modulators, STAT1 and IFIH1. A putative regulatory module was reconstructed by our method, revealing an intriguing feed-forward loop, in which STAT1 regulates IFIH1 and they both negatively regulate IFNAR1. STAT1 regulation on IFNAR1 was object of experimental validation at the protein level. CONCLUSIONS: Detailed description of the experimental set-up and of the analysis procedure is reported, with the intent to be of inspiration for other scientists who want to realize similar experiments to reconstruct gene regulatory modules starting from perturbations of possible regulators. Application of our approach to the study of IFN-α transcriptional response modulators in endothelial cells has led to many interesting novel findings and new biological hypotheses worth of validation.

Decreased VLDL-Apo B 100 Fractional Synthesis Rate Despite Hypertriglyceridemia in Subjects With Type 2 Diabetes and Nephropathy.

CONTEXT: Subjects with type 2 diabetes mellitus (T2DM) and diabetic nephropathy (DN) often exhibit hypertriglyceridemia. The mechanism(s) of such an increase are poorly known. OBJECTIVE: We investigated very low-density lipoprotein (VLDL)-Apo B 100 kinetics in T2DM subjects with and without DN, and in healthy controls. DESIGN: Stable isotope (13)C-leucine infusion and modeling analysis of tracer-to-tracee ratio dynamics in the protein product pool in the 6-8-h period following tracer infusion were employed. SETTING: Male subjects affected by T2DM, either with (n = 9) or without (n = 5) DN, and healthy male controls (n = 6), were studied under spontaneous glycemic levels in the post-absorptive state. RESULTS: In the T2DM patients with DN, plasma triglyceride (TG) (mean ± SD; 2.2 ± 0.8 mmol/L) and VLDL-Apo B 100 (17.4 ± 10.4 mg/dL) concentrations, and VLDL-Apo B 100 pool (0.56 ± 0.29 g), were ∼60-80% greater (P < .05 or less) than those of the T2DM subjects without DN (TG, 1.4 ± 0.5 mmol/L; VLDL-Apo B 100, 9.9 ± 2.5 mg/dL; VLDL-Apo B 100 pool, 0.36 ± 0.09 g), and ∼80-110% greater (P < .04 or less) than those of nondiabetic controls (TG, 1.2 ± 0.4 mmol/L; VLDL-Apo B 100, 8.2 ± 1.7 mg/dL; VLDL-Apo B 100, 0.32 ± 0.09 g). In sharp contrast however, in the subjects with T2DM and DN, VLDL-Apo B 100 fractional synthesis rate was ≥50% lower (4.8 ± 2.2 pools/d) than that of either the T2DM subjects without DN (9.9 ± 4.3 pools/d; P < .025) or the control subjects (12.5 ± 9.1 pools/d; P < .04). CONCLUSIONS: The hypertriglyceridemia of T2DM patients with DN is not due to hepatic VLDL-Apo B 100 overproduction, which is decreased, but it should be attributed to decreased apolipoprotein removal.

Compression and fast retrieval of SNP data.

MOTIVATION: The increasing interest in rare genetic variants and epistatic genetic effects on complex phenotypic traits is currently pushing genome-wide association study design towards datasets of increasing size, both in the number of studied subjects and in the number of genotyped single nucleotide polymorphisms (SNPs). This, in turn, is leading to a compelling need for new methods for compression and fast retrieval of SNP data. RESULTS: We present a novel algorithm and file format for compressing and retrieving SNP data, specifically designed for large-scale association studies. Our algorithm is based on two main ideas: (i) compress linkage disequilibrium blocks in terms of differences with a reference SNP and (ii) compress reference SNPs exploiting information on their call rate and minor allele frequency. Tested on two SNP datasets and compared with several state-of-the-art software tools, our compression algorithm is shown to be competitive in terms of compression rate and to outperform all tools in terms of time to load compressed data. AVAILABILITY AND IMPLEMENTATION: Our compression and decompression algorithms are implemented in a C++ library, are released under the GNU General Public License and are freely downloadable from http://www.dei.unipd.it/~sambofra/snpack.html.

Novel genetic susceptibility loci for diabetic end-stage renal disease identified through robust naive Bayes classification.

AIMS/HYPOTHESIS: Diabetic nephropathy is a major diabetic complication, and diabetes is the leading cause of end-stage renal disease (ESRD). Family studies suggest a hereditary component for diabetic nephropathy. However, only a few genes have been associated with diabetic nephropathy or ESRD in diabetic patients. Our aim was to detect novel genetic variants associated with diabetic nephropathy and ESRD. METHODS: We exploited a novel algorithm, 'Bag of Naive Bayes', whose marker selection strategy is complementary to that of conventional genome-wide association models based on univariate association tests. The analysis was performed on a genome-wide association study of 3,464 patients with type 1 diabetes from the Finnish Diabetic Nephropathy (FinnDiane) Study and subsequently replicated with 4,263 type 1 diabetes patients from the Steno Diabetes Centre, the All Ireland-Warren 3-Genetics of Kidneys in Diabetes UK collection (UK-Republic of Ireland) and the Genetics of Kidneys in Diabetes US Study (GoKinD US). RESULTS: Five genetic loci (WNT4/ZBTB40-rs12137135, RGMA/MCTP2-rs17709344, MAPRE1P2-rs1670754, SEMA6D/SLC24A5-rs12917114 and SIK1-rs2838302) were associated with ESRD in the FinnDiane study. An association between ESRD and rs17709344, tagging the previously identified rs12437854 and located between the RGMA and MCTP2 genes, was replicated in independent case-control cohorts. rs12917114 near SEMA6D was associated with ESRD in the replication cohorts under the genotypic model (p < 0.05), and rs12137135 upstream of WNT4 was associated with ESRD in Steno. CONCLUSIONS/INTERPRETATION: This study supports the previously identified findings on the RGMA/MCTP2 region and suggests novel susceptibility loci for ESRD. This highlights the importance of applying complementary statistical methods to detect novel genetic variants in diabetic nephropathy and, in general, in complex diseases.

Low-frequency rTMS inhibitory effects in the primary motor cortex: Insights from TMS-evoked potentials.

The neuromodulatory effects of repetitive transcranial magnetic stimulation (rTMS) have been mostly investigated by peripheral motor-evoked potentials (MEPs). New TMS-compatible EEG systems allow a direct investigation of the stimulation effects through the analysis of TMS-evoked potentials (TEPs). We investigated the effects of 1-Hz rTMS over the primary motor cortex (M1) of 15 healthy volunteers on TEP evoked by single pulse TMS over the same area. A second experiment in which rTMS was delivered over the primary visual cortex (V1) of 15 healthy volunteers was conducted to examine the spatial specificity of the effects. Single-pulse TMS evoked four main components: P30, N45, P60 and N100. M1-rTMS resulted in a significant decrease of MEP amplitude and in a significant increase of P60 and N100 amplitude. There was no effect after V1-rTMS. 1-Hz rTMS appears to increase the amount of inhibition following a TMS pulse, as demonstrated by the higher N100 and P60, which are thought to originate from GABAb-mediated inhibitory post-synaptic potentials. Our results confirm the reliability of the TMS-evoked N100 as a marker of cortical inhibition and provide insight into the neuromodulatory effects of 1-Hz rTMS. The present finding could be of relevance for therapeutic and diagnostic purposes.

Advancing our understanding of the glucose system via modeling: a perspective.

The glucose story begins with Claude Bernard's discovery of glycogen and milieu interieur, continued with Banting's and Best's discovery of insulin and with Rudolf Schoenheimer's paradigm of dynamic body constituents. Tracers and compartmental models allowed moving to the first quantitative pictures of the system and stimulated important developments in terms of modeling methodology. Three classes of multiscale models, models to measure, models to simulate, and models to control the glucose system, are reviewed in their historical development with an eye to the future.

The oral minimal model method.

The simultaneous assessment of insulin action, secretion, and hepatic extraction is key to understanding postprandial glucose metabolism in nondiabetic and diabetic humans. We review the oral minimal method (i.e., models that allow the estimation of insulin sensitivity, ß-cell responsivity, and hepatic insulin extraction from a mixed-meal or an oral glucose tolerance test). Both of these oral tests are more physiologic and simpler to administer than those based on an intravenous test (e.g., a glucose clamp or an intravenous glucose tolerance test). The focus of this review is on indices provided by physiological-based models and their validation against the glucose clamp technique. We discuss first the oral minimal model method rationale, data, and protocols. Then we present the three minimal models and the indices they provide. The disposition index paradigm, a widely used ß-cell function metric, is revisited in the context of individual versus population modeling. Adding a glucose tracer to the oral dose significantly enhances the assessment of insulin action by segregating insulin sensitivity into its glucose disposal and hepatic components. The oral minimal model method, by quantitatively portraying the complex relationships between the major players of glucose metabolism, is able to provide novel insights regarding the regulation of postprandial metabolism.

Leucine modulates dynamic phosphorylation events in insulin signaling pathway and enhances insulin-dependent glycogen synthesis in human skeletal muscle cells.

BACKGROUND: Branched-chain amino acids, especially leucine, are known to interact with insulin signaling pathway and glucose metabolism. However, the mechanism by which this is exerted, remain to be clearly defined. In order to examine the effect of leucine on muscle insulin signaling, a set of experiments was carried out to quantitate phosphorylation events along the insulin signaling pathway in human skeletal muscle cell cultures. Cells were exposed to insulin, leucine or both, and phosphorylation events of key insulin signaling molecules were tracked over time so as to monitor time-related responses that characterize the signaling events and could be missed by a single sampling strategy limited to pre/post stimulus events. RESULTS: Leucine is shown to increase the magnitude of insulin-dependent phosphorylation of protein kinase B (AKT) at Ser473 and glycogen synthase kinase (GSK3ß) at Ser21-9. Glycogen synthesis follows the same pattern of GSK3ß, with a significant increase at 100 µM leucine plus insulin stimulus. Moreover, data do not show any statistically significant increase of pGSK3ß and glycogen synthesis at higher leucine concentrations. Leucine is also shown to increase the magnitude of insulin-mediated extracellularly regulated kinase (ERK) phosphorylation; however, differently from AKT and GSK3ß, ERK shows a transient behavior, with an early peak response, followed by a return to the baseline condition. CONCLUSIONS: These experiments demonstrate a complementary effect of leucine on insulin signaling in a human skeletal muscle cell culture, promoting insulin-activated GSK3ß phosphorylation and glycogen synthesis.