We previously showed that a single bolus of "doubly-labeled" water ((2)H2 (18)O) can be used to simultaneously determine energy expenditure and turnover rates (synthesis and degradation) of tissue-specific lipids and proteins by modeling labeling patterns of protein-bound alanine and triglyceride-bound glycerol (Bederman IR, Dufner DA, Alexander JC, Previs SF. Am J Physiol Endocrinol Metab 290: E1048-E1056, 2006). Using this novel method, we quantified changes in the whole body and tissue-specific energy balance in a rat model of simulated "microgravity" induced by hindlimb suspension unloading (HSU). After chronic HSU (3 wk), rats exhibited marked atrophy of skeletal and cardiac muscles and significant decrease in adipose tissue mass. For example, soleus muscle mass progressively decreased 11, 43, and 52%. We found similar energy expenditure between control (90 ± 3 kcal · kg(-1)· day(-1)) and hindlimb suspended (81 ± 6 kcal/kg day) animals. By comparing food intake (∼ 112 kcal · kg(-1) · day(-1)) and expenditure, we found that animals maintained positive calorie balance proportional to their body weight. From multicompartmental fitting of (2)H-labeling patterns, we found significantly (P < 0.005) decreased rates of synthesis (percent decrease from control: cardiac, 25.5%; soleus, 70.3%; extensor digitorum longus, 44.9%; gastrocnemius, 52.5%; and adipose tissue, 39.5%) and rates of degradation (muscles: cardiac, 9.7%; soleus, 52.0%; extensor digitorum longus, 27.8%; gastrocnemius, 37.4%; and adipose tissue, 50.2%). Overall, HSU affected growth of young rats by decreasing the turnover rates of proteins in skeletal and cardiac muscles and adipose tissue triglycerides. Specifically, we found that synthesis rates of skeletal and cardiac muscle proteins were affected to a much greater degree compared with the decrease in degradation rates, resulting in large negative balance and significant tissue loss. In contrast, we found a small decrease in adipose tissue triglyceride synthesis paired with a large decrease in degradation, resulting in smaller negative energy balance and loss of fat mass. We conclude that HSU in rats differentially affects turnover of muscle proteins vs. adipose tissue triglycerides.
Cystic fibrosis (CF) mouse models exhibit exocrine pancreatic function, yet they do not develop adipose stores to the levels of non-CF mice. CF mice homozygous for the Cftr mutation (F508del) at 3 wk (postweaning) and 6 wk (young adult) of age had markedly less adipose tissue than non-CF mice. Food intake was markedly lower in 3-wk-old CF mice but normalized by 6 wk of age. Both 3- and 6-wk-old mice had dietary lipid absorption and fecal lipid excretion comparable to non-CF mice. Hepatic de novo lipogenesis (DNL), determined by (2)H incorporation, was reduced in CF mice. At 3 wk, F508del mice had significantly decreased DNL of palmitate and stearate, by 83% and 80%, respectively. By 6 wk, DNL rates in non-CF mice remained unchanged compared with 3-wk-old mice, while DNL rates of F508del mice were still reduced, by 33% and 40%, respectively. Adipose tissue fatty acid (FA) profiles were comparable in CF and non-CF mice, indicating that adipose differences are quantitative, not qualitative. A correspondingly lower content of (2)H-labeled FA was found in CF adipose tissue, consistent with reduced deposition of newly made hepatic triglycerides and/or decreased adipose tissue lipogenesis. Hepatic transcriptome analysis revealed lower mRNA expression from several genes involved in FA biosynthesis, suggesting downregulation of this pathway as a mechanism for the reduced lipogenesis. These novel data provide a model for altered lipid metabolism in CF, independent of malabsorption, and may partly explain the inability of pancreatic enzyme replacement therapy to completely restore normal body mass to CF patients.
Metabolomics is a relatively new “omics” platform, which analyzes a discrete set of metabolites detected in bio-fluids or tissue samples of organisms. It has been used in a diverse array of studies to detect biomarkers and to determine activity rates for pathways based on changes due to disease or drugs. Recent improvements in analytical methodology and large sample throughput allow for creation of large datasets of metabolites that reflect changes in metabolic dynamics due to disease or a perturbation in the metabolic network. However, current methods of comprehensive analyses of large metabolic datasets (metabolomics) are limited, unlike other “omics” approaches where complex techniques for analyzing coexpression/coregulation of multiple variables are applied. This paper discusses the shortcomings of current metabolomics data analysis techniques, and proposes a new multivariate technique (ADEMA) based on mutual information to identify expected metabolite level changes with respect to a specific condition. We show that ADEMA better predicts De Novo Lipogenesis pathway metabolite level changes in samples with Cystic Fibrosis (CF) than prediction based on the significance of individual metabolite level changes. We also applied ADEMA's classification scheme on three different cohorts of CF and wildtype mice. ADEMA was able to predict whether an unknown mouse has a CF or a wildtype genotype with 1.0, 0.84, and 0.9 accuracy for each respective dataset. ADEMA results had up to 31% higher accuracy as compared to other classification algorithms. In conclusion, ADEMA advances the state-of-the-art in metabolomics analysis, by providing accurate and interpretable classification results.
With the recent advances in experimental technologies, such as gas chromatography and mass spectrometry, the number of metabolites that can be measured in biofluids of individuals has markedly increased. Given a set of such measurements, a very common task encountered by biologists is to identify the metabolic mechanisms that lead to changes in the concentrations of given metabolites and interpret the metabolic consequences of the observed changes in terms of physiological problems, nutritional deficiencies, or diseases. In this paper, we present the steady-state metabolic network dynamics analysis (SMDA) approach in detail, together with its application in a cystic fibrosis study. We also present a computational performance evaluation of the SMDA tool against a mammalian metabolic network database. The query output space of the SMDA tool is exponentially large in the number of reactions of the network. However, (i) larger numbers of observations exponentially reduce the output size, and (ii) exploratory search and browsing of the query output space is provided to allow users to search for what they are looking for.
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