The investigation of intracellular metabolism is the mainstay in the biotechnology and physiology settings. Intracellular metabolic rates are commonly evaluated using labeling pattern of the identified metabolites obtained from stable isotope labeling experiments. The labeling pattern or mass distribution vector describes the fractional abundances of all isotopologs with different masses as a result of isotopic labeling, which are typically resolved using mass spectrometry. Because naturally occurring isotopes and isotopic impurity also contribute to measured signals, the measured patterns must be corrected to obtain the labeling patterns. Since contaminant isotopologs with the same nominal mass can be resolved using modern mass spectrometers with high mass resolution, the correction process should be resolution dependent. Here we present a software tool, ElemCor, to perform correction of such data in a resolution-dependent manner. The tool is based on mass difference theory (MDT) and information from unlabeled samples (ULS) to account for resolution effects. MDT is a mathematical theory and only requires chemical formulae to perform correction. ULS is semi-empirical and requires additional measurement of isotopologs from unlabeled samples. We validate both methods and show their improvement in accuracy and comprehensiveness over existing methods using simulated data and experimental data from Saccharomyces cerevisiae. The tool is available at https://github.com/4dsoftware/elemcor . We present a software tool based on two methods, MDT and ULS, to correct LC-MS data from isotopic labeling experiments for natural abundance and isotopic impurity. We recommend MDT for low-mass compounds for cost efficiency in experiments, and ULS for high-mass compounds with relatively large spectral inaccuracy that can be tracked by unlabeled standards.

As the burden of type 2 diabetes mellitus (T2DM) grows in the 21st century, the need to understand glucose metabolism heightens. Increased gluconeogenesis is a major contributor to the hyperglycemia seen in T2DM. Isotope tracer experiments in humans and animals over several decades have offered insights into gluconeogenesis under euglycemic and diabetic conditions. This review focuses on the current understanding of carbon flux in gluconeogenesis, including substrate contribution of various gluconeogenic precursors to glucose production. Alterations of gluconeogenic metabolites and fluxes in T2DM are discussed. We also highlight ongoing knowledge gaps in the literature that require further investigation. A comprehensive analysis of gluconeogenesis may enable a better understanding of T2DM pathophysiology and identification of novel targets for treating hyperglycemia.

Fasting results in major metabolic changes including a switch from glycogenolysis to gluconeogenesis to maintain glucose homeostasis. However, the relationship between the length of fasting and the relative contribution of gluconeogenic substrates remains unclear. We investigated the relative contribution of glycogen, lactate, and glycerol in glucose production of male C57BL/6 J-albino mice after 6, 12, and 18 h of fasting. We used non-perturbative infusions of 13C3 lactate, 13C3 glycerol, and 13C6 glucose combined with liquid chromatography mass spectrometry and metabolic flux analysis to study the contribution of substrates in gluconeogenesis (GNG). During infusion studies, both lactate and glycerol significantly label about 60% and 30-50% glucose carbon, respectively, but glucose labels much more lactate (∼90%) than glycerol carbon (∼10%). Our analyses indicate that lactate, but not glycerol is largely recycled during all fasting periods such that lactate is the largest direct contributor to GNG via the Cori cycle but a minor source of new glucose carbon (overall contribution). In contrast, glycerol is not only a significant direct contributor to GNG but also the largest overall contributor to GNG regardless of fasting length. Prolonged fasting decreases both the whole body turnover rate of glucose and lactate but increases that of glycerol, indicating that the usage of glycerol in GNG become more significant with longer fasting. Collectively, these findings suggest that glycerol is the dominant overall contributor of net glucose carbon in GNG during both short and prolonged fasting.

Gluconeogenesis is a critical biosynthetic process that helps maintain whole-body glucose homeostasis and becomes altered in certain medical diseases. We review gluconeogenic flux in various medical diseases, including common metabolic disorders, hormonal imbalances, specific inborn genetic errors, and cancer. We discuss how the altered gluconeogenic activity contributes to disease pathogenesis using data from experiments using isotopic tracer and spectroscopy methodologies. These in vitro, animal, and human studies provide insights into the changes in circulating levels of available gluconeogenesis substrates and the efficiency of converting those substrates to glucose by gluconeogenic organs. We highlight ongoing knowledge gaps, discuss emerging research areas, and suggest future investigations. A better understanding of altered gluconeogenesis flux may ultimately identify novel and targeted treatment strategies for such diseases.

Background: Thyroid hormone (TH) action is mediated by three major thyroid hormone receptor (THR) isoforms α1, β1, and β2 (THRA1, THRB1, and THRB2). These THRs and a fourth major but non-TH binding isoform, THRA2, are encoded by two genes Thra and Thrb . Reliable antibodies against all THR isoforms are not available, and THR isoform protein levels in mammalian tissues are often inferred from messenger RNA (mRNA) levels. Methods: We generated knock-in mouse models expressing endogenously and identically 2X hemagglutenin epitope (HA)-tagged THRs (THRA1/2, THRB1, and THRB2), which could then be detected by commercially available anti-HA antibodies. Using nuclear enrichment, immunoprecipitation, and Western blotting, we determined relative THR protein expression in 16 mouse organs. Results: In all peripheral organs tested except the liver, the predominant THR isoform was THRA1. Surprisingly, in metabolically active organs such as fat and muscle, THRB1 protein levels were up to 10 times lower than that of THRA1, while their mRNA levels appeared similar. In contrast to peripheral organs, the central nervous system (CNS) had a unique pattern with relatively low levels of both THRB1 and THRA1, and high levels of THRA2 expression. As expected, THRB2 was highly expressed in the pituitary, but a previously unknown sex-specific difference in THRB2 expression was found (female mice having higher pituitary expression than male mice). Higher THRB2 expression appears to make the central axis more sensitive to TH as both serum thyrotropin and Tshb mRNA levels were lower in female mice. Conclusions: Direct comparison of THR protein abundance in different organs using endogenously tagged HA-THR mouse lines shows that expression of THR isoforms is regulated at transcriptional and posttranscriptional levels, and in organ-specific manner. The prevalence of THRA1 and low abundance of THRB1 in majority of peripheral tissues suggest that peripheral actions of these isoforms should be revisited. A unique pattern of high THRA2 in CNS warrants further exploration of this non-TH binding isoform in brain development. Finally, THRB2, in addition to cell-specific control, is also regulated in a sex-specific manner, which may change the hypothalamus–pituitary–thyroid axis set point and perhaps metabolism in males and females.

Metabolic flux analysis (MFA) is an increasingly important tool to study metabolism quantitatively. Unlike the concentrations of metabolites, the fluxes, which are the rates at which intracellular metabolites interconvert, are not directly measurable. MFA uses stable isotope labeled tracers to reveal information related to the fluxes. The conceptual idea of MFA is that in tracer experiments the isotope labeling patterns of intracellular metabolites are determined by the fluxes, therefore by measuring the labeling patterns we can infer the fluxes in the network. In this review, we will discuss the basic concept of MFA using a simplified upper glycolysis network as an example. We will show how the fluxes are reflected in the isotope labeling patterns. The central idea we wish to deliver is that under metabolic and isotopic steady-state the labeling pattern of a metabolite is the flux-weighted average of the substrates’ labeling patterns. As a result, MFA can tell the relative contributions of converging metabolic pathways only when these pathways make substrates in different labeling patterns for the shared product. This is the fundamental principle guiding the design of isotope labeling experiment for MFA including tracer selection. In addition, we will also discuss the basic biochemical assumptions of MFA, and we will show the flux-solving procedure and result evaluation. Finally, we will highlight the link between isotopically stationary and nonstationary flux analysis.