The application of 2H2O to measure skeletal muscle protein synthesis

Skeletal muscle protein synthesis has generally been determined by the precursor:product labeling approach using labeled amino acids (e.g., [13C]leucine or [13C]-, [15N]-, or [2H]phenylalanine) as the tracers. Although reliable for determining rates of protein synthesis, this methodological approach requires experiments to be conducted in a controlled environment, and as a result, has limited our understanding of muscle protein renewal under free-living conditions over extended periods of time (i.e., integrative/cumulative assessments). An alternative tracer, 2H2O, has been successfully used to measure rates of muscle protein synthesis in mice, rats, fish and humans. Moreover, perturbations such as feeding and exercise have been included in these measurements without exclusion of common environmental and biological factors. In this review, we discuss the principle behind using 2H2O to measure muscle protein synthesis and highlight recent investigations that have examined the effects of feeding and exercise. The framework provided in this review should assist muscle biologists in designing experiments that advance our understanding of conditions in which anabolism is altered (e.g., exercise, feeding, growth, debilitating and metabolic pathologies)

Insulin Concentration Modulates Hepatic Lipid Accumulation in Mice in Part via Transcriptional Regulation of Fatty Acid Transport Proteins

Fatty liver disease (FLD) is commonly associated with insulin resistance and obesity, but interestingly it is also observed at low insulin states, such as prolonged fasting. Thus, we asked whether insulin is an independent modulator of hepatic lipid accumulation.In mice we induced, hypo- and hyperinsulinemia associated FLD by diet induced obesity and streptozotocin treatment, respectively. The mechanism of free fatty acid induced steatosis was studied in cell culture with mouse liver cells under different insulin concentrations, pharmacological phosphoinositol-3-kinase (PI3K) inhibition and siRNA targeted gene knock-down. We found with in vivo and in vitro models that lipid storage is increased, as expected, in both hypo- and hyperinsulinemic states, and that it is mediated by signaling through either insulin receptor substrate (IRS) 1 or 2. As previously reported, IRS-1 was up-regulated at high insulin concentrations, while IRS-2 was increased at low levels of insulin concentration. Relative increase in either of these insulin substrates, was associated with an increase in liver-specific fatty acid transport proteins (FATP) 2&5, and increased lipid storage. Furthermore, utilizing pharmacological PI3K inhibition we found that the IRS-PI3K pathway was necessary for lipogenesis, while FATP responses were mediated via IRS signaling. Data from additional siRNA experiments showed that knock-down of IRSs impacted FATP levels.States of perturbed insulin signaling (low-insulin or high-insulin) both lead to increased hepatic lipid storage via FATP and IRS signaling. These novel findings offer a common mechanism of FLD pathogenesis in states of both inadequate (prolonged fasting) and ineffective (obesity) insulin signaling

Peripheral Effects of FAAH Deficiency on Fuel and Energy Homeostasis: Role of Dysregulated Lysine Acetylation

FAAH (fatty acid amide hydrolase), primarily expressed in the liver, hydrolyzes the endocannabinoids fatty acid ethanolamides (FAA). Human FAAH gene mutations are associated with increased body weight and obesity. In our present study, using targeted metabolite and lipid profiling, and new global acetylome profiling methodologies, we examined the role of the liver on fuel and energy homeostasis in whole body FAAH(-/-) mice.FAAH(-/-) mice exhibit altered energy homeostasis demonstrated by decreased oxygen consumption (Indirect calorimetry). FAAH(-/-) mice are hyperinsulinemic and have adipose, skeletal and hepatic insulin resistance as indicated by stable isotope phenotyping (SIPHEN). Fed state skeletal muscle and liver triglyceride levels was increased 2-3 fold, while glycogen was decreased 42% and 57% respectively. Hepatic cholesterol synthesis was decreased 22% in FAAH(-/-) mice. Dysregulated hepatic FAAH(-/-) lysine acetylation was consistent with their metabolite profiling. Fasted to fed increases in hepatic FAAH(-/-) acetyl-CoA (85%, p<0.01) corresponded to similar increases in citrate levels (45%). Altered FAAH(-/-) mitochondrial malate dehydrogenase (MDH2) acetylation, which can affect the malate aspartate shuttle, was consistent with our observation of a 25% decrease in fed malate and aspartate levels. Decreased fasted but not fed dihydroxyacetone-P and glycerol-3-P levels in FAAH(-/-) mice was consistent with a compensating contribution from decreased acetylation of fed FAAH(-/-) aldolase B. Fed FAAH(-/-) alcohol dehydrogenase (ADH) acetylation was also decreased.Whole body FAAH deletion contributes to a pre-diabetic phenotype by mechanisms resulting in impairment of hepatic glucose and lipid metabolism. FAAH(-/-) mice had altered hepatic lysine acetylation, the pattern sharing similarities with acetylation changes reported with chronic alcohol treatment. Dysregulated hepatic lysine acetylation seen with impaired FAA hydrolysis could support the liver's role in fostering the pre-diabetic state, and may reflect part of the mechanism underlying the hepatic effects of endocannabinoids in alcoholic liver disease mouse models

Isotope Fractionation during Gas Chromatography Can Enhance Mass Spectrometry-Based Measures of 2H-Labeling of Small Molecules

Stable isotope tracers can be used to quantify the activity of metabolic pathways. Specifically, 2H-water is quite versatile, and its incorporation into various products can enable measurements of carbohydrate, lipid, protein and nucleic acid kinetics. However, since there are limits on how much 2H-water can be administered and since some metabolic processes may be slow, it is possible that one may be challenged with measuring small changes in isotopic enrichment. We demonstrate an advantage of the isotope fractionation that occurs during gas chromatography, namely, setting tightly bounded integration regions yields a powerful approach for determining isotope ratios. We determined how the degree of isotope fractionation, chromatographic peak width and mass spectrometer dwell time can increase the apparent isotope labeling. Relatively simple changes in the logic surrounding data acquisition and processing can enhance gas chromatography-mass spectrometry measures of low levels of 2H-labeling, this is especially useful when asymmetrical peaks are recorded at low signal:background. Although we have largely focused attention on alanine (which is of interest in studies of protein synthesis), it should be possible to extend the concepts to other analytes and/or hardware configurations

Gaussian Process Modeling of Protein Turnover

We describe a stochastic model to compute in vivo protein turnover rate constants from stable-isotope labeling and high-throughput liquid chromatography–mass spectrometry experiments. We show that the often-used one- and two-compartment nonstochastic models allow explicit solutions from the corresponding stochastic differential equations. The resulting stochastic process is a Gaussian processes with Ornstein–Uhlenbeck covariance matrix. We applied the stochastic model to a large-scale data set from ¹⁵N labeling and compared its performance metrics with those of the nonstochastic curve fitting. The comparison showed that for more than 99% of proteins, the stochastic model produced better fits to the experimental data (based on residual sum of squares). The model was used for extracting protein-decay rate constants from mouse brain (slow turnover) and liver (fast turnover) samples. We found that the most affected (compared to two-exponent curve fitting) results were those for liver proteins. The ratio of the median of degradation rate constants of liver proteins to those of brain proteins increased 4-fold in stochastic modeling compared to the two-exponent fitting. Stochastic modeling predicted stronger differences of protein turnover processes between mouse liver and brain than previously estimated. The model is independent of the labeling isotope. To show this, we also applied the model to protein turnover studied in induced heart failure in rats, in which metabolic labeling was achieved by administering heavy water. No changes in the model were necessary for adapting to heavy-water labeling. The approach has been implemented in a freely available R code

Gaussian Process Modeling of Protein Turnover

We describe a stochastic model to compute in vivo protein turnover rate constants from stable-isotope labeling and high-throughput liquid chromatography–mass spectrometry experiments. We show that the often-used one- and two-compartment nonstochastic models allow explicit solutions from the corresponding stochastic differential equations. The resulting stochastic process is a Gaussian processes with Ornstein–Uhlenbeck covariance matrix. We applied the stochastic model to a large-scale data set from ¹⁵N labeling and compared its performance metrics with those of the nonstochastic curve fitting. The comparison showed that for more than 99% of proteins, the stochastic model produced better fits to the experimental data (based on residual sum of squares). The model was used for extracting protein-decay rate constants from mouse brain (slow turnover) and liver (fast turnover) samples. We found that the most affected (compared to two-exponent curve fitting) results were those for liver proteins. The ratio of the median of degradation rate constants of liver proteins to those of brain proteins increased 4-fold in stochastic modeling compared to the two-exponent fitting. Stochastic modeling predicted stronger differences of protein turnover processes between mouse liver and brain than previously estimated. The model is independent of the labeling isotope. To show this, we also applied the model to protein turnover studied in induced heart failure in rats, in which metabolic labeling was achieved by administering heavy water. No changes in the model were necessary for adapting to heavy-water labeling. The approach has been implemented in a freely available R code