Reconstruction of insulin signal flow from phosphoproteome and metabolome data

Metabolic modeling with Big Data and the gut microbiome

The recent advances in high-throughput omics technologies have enabled researchers to explore the intricacies of the human microbiome. On the clinical front, the gut microbial community has been the focus of many biomarker-discovery studies. While the recent deluge of high-throughput data in microbiome research has been vastly informative and groundbreaking, we have yet to capture the full potential of omics-based approaches. Realizing the promise of multi-omics data will require integration of disparate omics data, as well as a biologically relevant, mechanistic framework - or metabolic model - on which to overlay these data. Also, a new paradigm for metabolic model evaluation is necessary. Herein, we outline the need for multi-omics data integration, as well as the accompanying challenges. Furthermore, we present a framework for characterizing the ecology of the gut microbiome based on metabolic network modeling.

Establishment of Protocols for Global Metabolomics by LC-MS for Biomarker Discovery

Metabolomics is a promising avenue for biomarker discovery. Although the quality of metabolomic analyses, especially global metabolomics (G-Met) using mass spectrometry (MS), largely depends on the instrumentation, potential bottlenecks still exist at several basic levels in the metabolomics workflow. Therefore, we established a precise protocol initially for the G-Met analyses of human blood plasma to overcome some these difficulties. In our protocol, samples are deproteinized in a 96-well plate using an automated liquid-handling system, and conducted either using a UHPLC-QTOF/MS system equipped with a reverse phase column or a LC-FTMS system equipped with a normal phase column. A normalization protocol of G-Met data was also developed to compensate for intra- and inter-batch differences, and the variations were significantly reduced along with our normalization, especially for the UHPLC-QTOF/MS data with a C18 reverse-phase column for positive ions. Secondly, we examined the changes in metabolomic profiles caused by the storage of EDTA-blood specimens to identify quality markers for the evaluation of the specimens’ pre-analytical conditions. Forty quality markers, including lysophospholipids, dipeptides, fatty acids, succinic acid, amino acids, glucose, and uric acid were identified by G-Met for the evaluation of plasma sample quality and established the equation of calculating the quality score. We applied our quality markers to a small-scale study to evaluate the quality of clinical samples. The G-Met protocols and quality markers established here should prove useful for the discovery and development of biomarkers for a wider range of diseases.

Integrative analysis of human omics data using biomolecular networks

High-throughput '-omics' technologies have given rise to an increasing abundance of genome-scale data detailing human biology at the molecular level. Although these datasets have already made substantial contributions to a more comprehensive understanding of human physiology and diseases, their interpretation becomes increasingly cryptic and nontrivial as they continue to expand in size and complexity. Systems biology networks offer a scaffold upon which omics data can be integrated, facilitating the extraction of new and physiologically relevant information from the data. Two of the most prevalent networks that have been used for such integrative analyses of omics data are genome-scale metabolic models (GEMs) and protein-protein interaction (PPI) networks, both of which have demonstrated success among many different omics and sample types. This integrative approach seeks to unite 'top-down' omics data with 'bottom-up' biological networks in a synergistic fashion that draws on the strengths of both strategies. As the volume and resolution of high-throughput omics data continue to grow, integrative network-based analyses are expected to play an increasingly important role in their interpretation.

Effects of insulin on peripheral nerves

AIMS

To assess the effects of insulin on peripheral nerve under normoglycemic and hyperglycemic conditions in the presence and absence of anoxia.

METHODS

This study uses the in-vitro sciatic nerve model to assess the effect of insulin on peripheral nerve with the nerve action potential (NAP) as an index of nerve function.

RESULTS

Under normoglycemic conditions, low concentrations of regular insulin (0.01nM) reduced the conduction velocity of oxygenated nerves. Hyperglycemia increased the duration of the NAP and this increase was nearly completely eliminated by insulin in the 0.1nM-100nM concentration range. Insulin (1nM) also had effects on normoglycemic nerves exposed to intermittent anoxia, producing a decrease in the paired-pulse response and NAP amplitude and an increase in peak duration. This was associated with a reduced time to anoxia-induced conduction block. Similar effects were seen when regular insulin was replaced by insulin detemir, but the latter required much higher concentrations.

CONCLUSIONS

Insulin has concentration dependent effects on the peripheral nerve that are dependent on glucose and anoxia. These effects may be important in modulating neuropathic consequences of diabetes.

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).

Phosphoprotein network analysis of white adipose tissues unveils deregulated pathways in response to high-fat diet

Despite efforts in the last decade, signaling aberrations associated with obesity remain poorly understood. To dissect molecular mechanisms that define this complex metabolic disorder, we carried out global phosphoproteomic analysis of white adipose tissue (WAT) from mice fed on low-fat diet (LFD) and high-fat diet (HFD). We quantified phosphorylation levels on 7696 peptides, and found significant differential phosphorylation levels in 282 phosphosites from 191 proteins, including various insulin-responsive proteins and metabolic enzymes involved in lipid homeostasis in response to high-fat feeding. Kinase-substrate prediction and integrated network analysis of the altered phosphoproteins revealed underlying signaling modulations during HFD-induced obesity, and suggested deregulation of lipogenic and lipolytic pathways. Mutation of the differentially-regulated novel phosphosite on cytoplasmic acetyl-coA forming enzyme ACSS2 (S263A) upon HFD-induced obesity led to accumulation of serum triglycerides and reduced insulin-responsive AKT phosphorylation as compared to wild type ACSS2, thus highlighting its role in obesity. Altogether, our study presents a comprehensive map of adipose tissue phosphoproteome in obesity and reveals many previously unknown candidate phosphorylation sites for future functional investigation.

Mathematical Modeling and Dynamic Simulation of Metabolic Reaction Systems Using Metabolome Time Series Data

The high-throughput acquisition of metabolome data is greatly anticipated for the complete understanding of cellular metabolism in living organisms. A variety of analytical technologies have been developed to acquire large-scale metabolic profiles under different biological or environmental conditions. Time series data are useful for predicting the most likely metabolic pathways because they provide important information regarding the accumulation of metabolites, which implies causal relationships in the metabolic reaction network. Considerable effort has been undertaken to utilize these data for constructing a mathematical model merging system properties and quantitatively characterizing a whole metabolic system in toto. However, there are technical difficulties between benchmarking the provision and utilization of data. Although, hundreds of metabolites can be measured, which provide information on the metabolic reaction system, simultaneous measurement of thousands of metabolites is still challenging. In addition, it is nontrivial to logically predict the dynamic behaviors of unmeasurable metabolite concentrations without sufficient information on the metabolic reaction network. Yet, consolidating the advantages of advancements in both metabolomics and mathematical modeling remain to be accomplished. This review outlines the conceptual basis of and recent advances in technologies in both the research fields. It also highlights the potential for constructing a large-scale mathematical model by estimating model parameters from time series metabolome data in order to comprehensively understand metabolism at the systems level.

Modulation of the cellular content of metabolites in adipocytes by insulin

Although the insulin-mediated cell signaling pathway has been extensively examined, changes in the cellular content of metabolites currently remain unclear. We herein examined metabolite contents in 3T3-L1 adipocytes treated with insulin using a metabolomic analysis. Fifty-four compounds were detected, and the contents of metabolites from the citric acid cycle increased in response to the insulin treatment for 4 h, which was sensitive to U0126 and LY294002, inhibitors for mitogen-activated protein kinase kinase-1 and phosphoinositide 3-kinase, respectively. The cellular contents of fumaric acid and malic acid were increased more by insulin than those of citric acid and succinic acid. Time-course changes in metabolites from the citric acid cycle exhibited oscillations with a 2-h cycle. A metabolic pathway analysis also indicated that insulin affected the metabolism of alanine, aspartate and glutamate, as well as that of arginine and proline. The contents of free amino acids were slightly decreased by the insulin treatment, while the co-treatment with U0126 and LY294002 abrogated these insulin-mediated decreases. The present study revealed the unexpected accumulation of citric acid cycle metabolites in adipocytes by insulin. Our results indicate the usefulness of metabolomic analyses for obtaining a more comprehensive understanding of the regulation of metabolic pathways in cell-culture systems.

Trans-Omics: How To Reconstruct Biochemical Networks Across Multiple 'Omic' Layers

We propose 'trans-omic' analysis for reconstructing global biochemical networks across multiple omic layers by use of both multi-omic measurements and computational data integration. We introduce technologies for connecting multi-omic data based on prior knowledge of biochemical interactions and characterize a biochemical trans-omic network by concepts of a static and dynamic nature. We introduce case studies of metabolism-centric trans-omic studies to show how to reconstruct a biochemical trans-omic network by connecting multi-omic data and how to analyze it in terms of the static and dynamic nature. We propose a trans-ome-wide association study (trans-OWAS) connecting phenotypes with trans-omic networks that reflect both genetic and environmental factors, which can characterize several complex lifestyle diseases as breakdowns in the trans-omic system.

Protein Phosphorylation: A Major Switch Mechanism for Metabolic Regulation

Metabolism research is undergoing a renaissance because many diseases are increasingly recognized as being characterized by perturbations in intracellular metabolic regulation. Metabolic changes can be conferred through changes to the expression of metabolic enzymes, the concentrations of substrates or products that govern reaction kinetics, or post-translational modification (PTM) of the proteins that facilitate these reactions. On the 60th anniversary since its discovery, reversible protein phosphorylation is widely appreciated as an essential PTM regulating metabolism. With the ability to quantitatively measure dynamic changes in protein phosphorylation on a global scale - hereafter referred to as phosphoproteomics - we are now entering a new era in metabolism research, with mass spectrometry (MS)-based proteomics at the helm.

Mosaic gene network modelling identified new regulatory mechanisms in HCV infection

Modelling of gene networks is widely used in systems biology to study the functioning of complex biological systems. Most of the existing mathematical modelling techniques are useful for analysis of well-studied biological processes, for which information on rates of reactions is available. However, complex biological processes such as those determining the phenotypic traits of organisms or pathological disease processes, including pathogen-host interactions, involve complicated cross-talk between interacting networks. Furthermore, the intrinsic details of the interactions between these networks are often missing. In this study, we developed an approach, which we call mosaic network modelling, that allows the combination of independent mathematical models of gene regulatory networks and, thereby, description of complex biological systems. The advantage of this approach is that it allows us to generate the integrated model despite the fact that information on molecular interactions between parts of the model (so-called mosaic fragments) might be missing. To generate a mosaic mathematical model, we used control theory and mathematical models, written in the form of a system of ordinary differential equations (ODEs). In the present study, we investigated the efficiency of this method in modelling the dynamics of more than 10,000 simulated mosaic regulatory networks consisting of two pieces. Analysis revealed that this approach was highly efficient, as the mean deviation of the dynamics of mosaic network elements from the behaviour of the initial parts of the model was less than 10%. It turned out that for construction of the control functional, data on perturbation of one or two vertices of the mosaic piece are sufficient. Further, we used the developed method to construct a mosaic gene regulatory network including hepatitis C virus (HCV) as the first piece and the tumour necrosis factor (TNF)-induced apoptosis and NF-κB induction pathways as the second piece. Thus, the mosaic model integrates the model of HCV subgenomic replicon replication with the model of TNF-induced apoptosis and NF-κB induction. Analysis of the mosaic model revealed that the regulation of TNF-induced signaling by the HCV network is crucially dependent on the RIP1, TRADD, TRAF2, FADD, IKK, IκBα, c-FLIP, and BAR genes. Overall, the developed mosaic gene network modelling approach demonstrated good predictive power and allowed the prediction of new regulatory nodes in HCV action on apoptosis and the NF-κB pathway. Those theoretical predictions could be a basis for further experimental verification.

Posttranslational regulation of microbial metabolism

Fluxes in microbial metabolism are controlled by various regulatory layers that alter abundance or activity of metabolic enzymes. Recent studies suggest a division of labor between these layers: transcriptional regulation mostly controls the allocation of protein resources, passive flux regulation by enzyme saturation and thermodynamics allows rapid responses at the expense of higher protein cost, and posttranslational regulation is utilized by cells to directly take control of metabolic decisions. We present recent advances in elucidating the role of these regulatory layers, focusing on posttranslational modifications and allosteric interactions. As the systematic mapping of posttranslational regulatory events has now become possible, the next challenge is to identify those regulatory events that are functionally relevant under a given condition.