Kinetic Trans-omic Analysis Reveals Key Regulatory Mechanisms for Insulin-Regulated Glucose Metabolism in Adipocytes

Metabolomics and Microbial Metabolism: Toward a Systematic Understanding

Over the past decades, our understanding of microbial metabolism has increased dramatically. Metabolomics, a family of techniques that are used to measure the quantities of small molecules in biological samples, has been central to these efforts. Advances in analytical chemistry have made it possible to measure the relative and absolute concentrations of more and more compounds with increasing levels of certainty. In this review, we highlight how metabolomics has contributed to understanding microbial metabolism and in what ways it can still be deployed to expand our systematic understanding of metabolism. To that end, we explain how metabolomics was used to (a) characterize network topologies of metabolism and its regulation networks, (b) elucidate the control of metabolic function, and (c) understand the molecular basis of higher-order phenomena. We also discuss areas of inquiry where technological advances should continue to increase the impact of metabolomics, as well as areas where our understanding is bottlenecked by other factors such as the availability of statistical and modeling frameworks that can extract biological meaning from metabolomics data.

Time-dependent cell-state selection identifies transiently expressed genes regulating ILC2 activation

The decision of whether cells are activated or not is controlled through dynamic intracellular molecular networks. However, the low population of cells during the transition state of activation renders the analysis of the transcriptome of this state technically challenging. To address this issue, we have developed the Time-Dependent Cell-State Selection (TDCSS) technique, which employs live-cell imaging of secretion activity to detect an index of the transition state, followed by the simultaneous recovery of indexed cells for subsequent transcriptome analysis. In this study, we used the TDCSS technique to investigate the transition state of group 2 innate lymphoid cells (ILC2s) activation, which is indexed by the onset of interleukin (IL)-13 secretion. The TDCSS approach allowed us to identify time-dependent genes, including transiently induced genes (TIGs). Our findings of IL4 and MIR155HG as TIGs have shown a regulatory function in ILC2s activation.

Optogenetic decoding of Akt2-regulated metabolic signaling pathways in skeletal muscle cells using transomics analysis

Insulin regulates various cellular metabolic processes by activating specific isoforms of the Akt family of kinases. Here, we elucidated metabolic pathways that are regulated in an Akt2-dependent manner. We constructed a transomics network by quantifying phosphorylated Akt substrates, metabolites, and transcripts in C2C12 skeletal muscle cells with acute, optogenetically induced activation of Akt2. We found that Akt2-specific activation predominantly affected Akt substrate phosphorylation and metabolite regulation rather than transcript regulation. The transomics network revealed that Akt2 regulated the lower glycolysis pathway and nucleotide metabolism and cooperated with Akt2-independent signaling to promote the rate-limiting steps in these processes, such as the first step of glycolysis, glucose uptake, and the activation of the pyrimidine metabolic enzyme CAD. Together, our findings reveal the mechanism of Akt2-dependent metabolic pathway regulation, paving the way for Akt2-targeting therapeutics in diabetes and metabolic disorders.

Stochastic simulation algorithm for isotope-based dynamic flux analysis

Carbon isotope labeling method is a standard metabolic engineering tool for flux quantification in living cells. To cope with the high dimensionality of isotope labeling systems, diverse algorithms have been developed to reduce the number of variables or operations in metabolic flux analysis (MFA), but lacks generalizability to non-stationary metabolic conditions. In this study, we present a stochastic simulation algorithm (SSA) derived from the chemical master equation of the isotope labeling system. This algorithm allows to compute the time evolution of isotopomer concentrations in non-stationary conditions, with the valuable property that computational time does not scale with the number of isotopomers. The efficiency and limitations of the algorithm is benchmarked for the forward and inverse problems of 13C-DMFA in the pentose phosphate pathways, and is compared with EMU-based methods for NMFA and MFA including the central carbon metabolism. Overall, SSA constitutes an alternative class to deterministic approaches for metabolic flux analysis that is well adapted to comprehensive dataset including parallel labeling experiments, and whose limitations associated to the sampling size can be overcome by using Monte Carlo sampling approaches.

Microscopic image-based classification of adipocyte differentiation by machine learning

Adipocyte differentiation is a sequential process involving increased expression of peroxisome proliferator-activated receptor gamma (PPARγ), adipocyte-specific gene expression, and accumulation of lipid droplets in the cytoplasm. Expression of the transcription factors involved is usually detected using canonical biochemical or biomolecular procedures such as Western blotting or qPCR of pooled cell lysates. While this provides a useful average index for adipogenesis for some populations, the precise stage of adipogenesis cannot be distinguished at the single-cell level, because the heterogenous nature of differentiation among cells limits the utility of averaged data. We have created a classifier to sort cells, and used it to determine the stage of adipocyte differentiation at the single-cell level. We used a machine learning method with microscopic images of cell stained for PPARγ and lipid droplets as input data. Our results show that the classifier can successfully determine the precise stage of differentiation. Stage classification and subsequent model fitting using the sequential reaction model revealed the action of pioglitazone and rosiglitazone to be promotion of transition from the stage of increased PPARγ expression to the next stage. This indicates that these drugs are PPARγ agonists, and that our classifier and model can accurately estimate drug action points and would be suitable for evaluating the stage/state of individual cells during differentiation or disease progression. The incorporation of both biochemical and morphological information derived from immunofluorescence image of cells and so overcomes limitations of current models.

Signaling and Gene Expression in Skeletal Muscles in Type 2 Diabetes: Current Results and OMICS Perspectives

Skeletal muscles mainly contribute to the emergence of insulin resistance, impaired glucose tolerance and the development of type 2 diabetes. Molecular mechanisms that regulate glucose uptake are diverse, including the insulin-dependent as most important, and others as also significant. They involve a wide range of proteins that control intracellular traffic and exposure of glucose transporters on the cell surface to create an extensive regulatory network. Here, we highlight advantages of the omics approaches to explore the insulin-regulated proteins and genes in human skeletal muscle with varying degrees of metabolic disorders. We discuss methodological aspects of the assessment of metabolic dysregulation and molecular responses of human skeletal muscle to insulin. The known molecular mechanisms of glucose uptake regulation and the first results of phosphoproteomic and transcriptomic studies are reviewed, which unveiled a large-scale array of insulin targets in muscle cells. They demonstrate that a clear depiction of changes that occur during metabolic dysfunction requires systemic and combined analysis at different levels of regulation, including signaling pathways, transcription factors, and gene expression. Such analysis seems promising to explore yet undescribed regulatory mechanisms of glucose uptake by skeletal muscle and identify the key regulators as potential therapeutic targets.

In vivo transomic analyses of glucose-responsive metabolism in skeletal muscle reveal core differences between the healthy and obese states

Metabolic regulation in skeletal muscle is essential for blood glucose homeostasis. Obesity causes insulin resistance in skeletal muscle, leading to hyperglycemia and type 2 diabetes. In this study, we performed multiomic analysis of the skeletal muscle of wild-type (WT) and leptin-deficient obese (ob/ob) mice, and constructed regulatory transomic networks for metabolism after oral glucose administration. Our network revealed that metabolic regulation by glucose-responsive metabolites had a major effect on WT mice, especially carbohydrate metabolic pathways. By contrast, in ob/ob mice, much of the metabolic regulation by glucose-responsive metabolites was lost and metabolic regulation by glucose-responsive genes was largely increased, especially in carbohydrate and lipid metabolic pathways. We present some characteristic metabolic regulatory pathways found in central carbon, branched amino acids, and ketone body metabolism. Our transomic analysis will provide insights into how skeletal muscle responds to changes in blood glucose and how it fails to respond in obesity.

Trans-omics analysis of insulin action reveals a cell growth subnetwork which co-regulates anabolic processes

Insulin signaling promotes anabolic metabolism to regulate cell growth through multi-omic interactions. To obtain a comprehensive view of the cellular responses to insulin, we constructed a trans-omic network of insulin action in Drosophila cells that involves the integration of multi-omic data sets. In this network, 14 transcription factors, including Myc, coordinately upregulate the gene expression of anabolic processes such as nucleotide synthesis, transcription, and translation, consistent with decreases in metabolites such as nucleotide triphosphates and proteinogenic amino acids required for transcription and translation. Next, as cell growth is required for cell proliferation and insulin can stimulate proliferation in a context-dependent manner, we integrated the trans-omic network with results from a CRISPR functional screen for cell proliferation. This analysis validates the role of a Myc-mediated subnetwork that coordinates the activation of genes involved in anabolic processes required for cell growth.

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A trans-omic network of insulin action in Drosophila cells was constructed

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Insulin co-regulates various anabolic processes in a time-dependent manner

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The trans-omic network and a CRISPR screen for cell proliferation were integrated

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A Myc-mediated subnetwork promoting anabolic processes is required for cell growth

Quantitative metabolic fluxes regulated by trans-omic networks

Cells change their metabolism in response to internal and external conditions by regulating the trans-omic network, which is a global biochemical network with multiple omic layers. Metabolic flux is a direct measure of the activity of a metabolic reaction that provides valuable information for understanding complex trans-omic networks. Over the past decades, techniques to determine metabolic fluxes, including 13C-metabolic flux analysis (13C-MFA), flux balance analysis (FBA), and kinetic modeling, have been developed. Recent studies that acquire quantitative metabolic flux and multi-omic data have greatly advanced the quantitative understanding and prediction of metabolism-centric trans-omic networks. In this review, we present an overview of 13C-MFA, FBA, and kinetic modeling as the main techniques to determine quantitative metabolic fluxes, and discuss their advantages and disadvantages. We also introduce case studies with the aim of understanding complex metabolism-centric trans-omic networks based on the determination of metabolic fluxes.

Integrating adipocyte insulin signaling and metabolism in the multi-omics era

Insulin stimulates glucose uptake into adipocytes via mTORC2/AKT signaling and GLUT4 translocation and directs glucose carbons into glycolysis, glycerol for TAG synthesis, and de novo lipogenesis. Adipocyte insulin resistance is an early indicator of type 2 diabetes in obesity, a worldwide health crisis. Thus, understanding the interplay between insulin signaling and central carbon metabolism pathways that maintains adipocyte function, blood glucose levels, and metabolic homeostasis is critical. While classically viewed through the lens of individual enzyme-substrate interactions, advances in mass spectrometry are beginning to illuminate adipocyte signaling and metabolic networks on an unprecedented scale, yet this is just the tip of the iceberg. Here, we review how 'omics approaches help to elucidate adipocyte insulin action in cellular time and space.

Multi-omics-based label-free metabolic flux inference reveals obesity-associated dysregulatory mechanisms in liver glucose metabolism

Glucose homeostasis is maintained by modulation of metabolic flux. Enzymes and metabolites regulate the involved metabolic pathways. Dysregulation of glucose homeostasis is a pathological event in obesity. Analyzing metabolic pathways and the mechanisms contributing to obesity-associated dysregulation in vivo is challenging. Here, we introduce OMELET: Omics-Based Metabolic Flux Estimation without Labeling for Extended Trans-omic Analysis. OMELET uses metabolomic, proteomic, and transcriptomic data to identify relative changes in metabolic flux, and to calculate contributions of metabolites, enzymes, and transcripts to the changes in metabolic flux. By evaluating the livers of fasting ob/ob mice, we found that increased metabolic flux through gluconeogenesis resulted primarily from increased transcripts, whereas that through the pyruvate cycle resulted from both increased transcripts and changes in substrates of metabolic enzymes. With OMELET, we identified mechanisms underlying the obesity-associated dysregulation of metabolic flux in the liver.

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We developed OMELET to infer metabolic flux from label-free multi-omic data

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Contributions of metabolites, enzymes, and transcripts for flux were inferred

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Gluconeogenic flux increased in fasting ob/ob mice by increased transcripts

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Increased pyruvate cycle fluxes were led by increased transcripts and substrates

An extensive and dynamic trans-omic network illustrating prominent regulatory mechanisms in response to insulin in the liver

An effective combination of multi-omic datasets can enhance our understanding of complex biological phenomena. To build a context-dependent network with multiple omic layers, i.e., a trans-omic network, we perform phosphoproteomics, transcriptomics, proteomics, and metabolomics of murine liver for 4 h after insulin administration and integrate the resulting time series. Structural characteristics and dynamic nature of the network are analyzed to elucidate the impact of insulin. Early and prominent changes in protein phosphorylation and persistent and asynchronous changes in mRNA and protein levels through non-transcriptional mechanisms indicate enhanced crosstalk between phosphorylation-mediated signaling and protein expression regulation. Metabolic response shows different temporal regulation with transient increases at early time points across categories and enhanced response in the amino acid and nucleotide categories at later time points as a result of process convergence. This extensive and dynamic view of the trans-omic network elucidates prominent regulatory mechanisms that drive insulin responses through intricate interlayer coordination.

Evidence of a hormonal reshuffle in the cecal metabolome fingerprint of a strain of rats resistant to decompression sickness

On one side, decompression sickness (DCS) with neurological disorders lead to a reshuffle of the fecal metabolome from rat caecum. On the other side, there is high inter-individual variability in terms of occurrence of DCS. One could wonder whether the fecal metabolome could be linked to the DCS-susceptibility. We decided to study male and female rats selected for their resistance to decompression sickness, and we hypothesize a strong impregnation concerning the fecal metabolome. The aim is to verify whether the rats resistant to the accident have a fecal metabolomic signature different from the stem generations sensitive to DCS. 39 DCS-resistant animals (21 females and 18 males), aged 14 weeks, were compared to 18 age-matched standard Wistar rats (10 females and 8 males), i.e., the same as those we used for the founding stock. Conventional and ChemRICH approaches helped the metabolomic interpretation of the 226 chemical compounds analyzed in the cecal content. Statistical analysis shows a panel of 81 compounds whose expression had changed following the selection of rats based on their resistance to DCS. 63 compounds are sex related. 39 are in common. This study shows the spectral fingerprint of the fecal metabolome from the caecum of a strain of rats resistant to decompression sickness. This study also confirms a difference linked to sex in the metabolome of non-selected rats, which disappear with selective breeding. Results suggest hormonal and energetic reshuffle, including steroids sugars or antibiotic compounds, whether in the host or in the microbial community.

Trans-omic analysis reveals obesity-associated dysregulation of inter-organ metabolic cycles between the liver and skeletal muscle

Systemic metabolic homeostasis is regulated by inter-organ metabolic cycles involving multiple organs. Obesity impairs inter-organ metabolic cycles, resulting in metabolic diseases. The systemic landscape of dysregulated inter-organ metabolic cycles in obesity has yet to be explored. Here, we measured the transcriptome, proteome, and metabolome in the liver and skeletal muscle and the metabolome in blood of fasted wild-type and leptin-deficient obese (ob/ob) mice, identifying components with differential abundance and differential regulation in ob/ob mice. By constructing and evaluating the trans-omic network controlling the differences in metabolic reactions between fasted wild-type and ob/ob mice, we provided potential mechanisms of the obesity-associated dysfunctions of metabolic cycles between liver and skeletal muscle involving glucose-alanine, glucose-lactate, and ketone bodies. Our study revealed obesity-associated systemic pathological mechanisms of dysfunction of inter-organ metabolic cycles.

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Multi-omic data in liver and skeletal muscle of WT and ob/ob mice were measured

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We developed the trans-omic network of differentially regulated metabolic reactions

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Dysregulation of inter-organ metabolic cycles associated with obesity was revealed

Transomics analysis reveals allosteric and gene regulation axes for altered hepatic glucose-responsive metabolism in obesity

Impaired glucose tolerance associated with obesity causes postprandial hyperglycemia and can lead to type 2 diabetes. To study the differences in liver metabolism in healthy and obese states, we constructed and analyzed transomics glucose-responsive metabolic networks with layers for metabolites, expression data for metabolic enzyme genes, transcription factors, and insulin signaling proteins from the livers of healthy and obese mice. We integrated multiomics time course data from wild-type and leptin-deficient obese (ob/ob) mice after orally administered glucose. In wild-type mice, metabolic reactions were rapidly regulated within 10 min of oral glucose administration by glucose-responsive metabolites, which functioned as allosteric regulators and substrates of metabolic enzymes, and by Akt-induced changes in the expression of glucose-responsive genes encoding metabolic enzymes. In ob/ob mice, the majority of rapid regulation by glucose-responsive metabolites was absent. Instead, glucose administration produced slow changes in the expression of carbohydrate, lipid, and amino acid metabolic enzyme-encoding genes to alter metabolic reactions on a time scale of hours. Few regulatory events occurred in both healthy and obese mice. Thus, our transomics network analysis revealed that regulation of glucose-responsive liver metabolism is mediated through different mechanisms in healthy and obese states. Rapid changes in allosteric regulators and substrates and in gene expression dominate the healthy state, whereas slow changes in gene expression dominate the obese state.