Adaptation of fuel selection to acute decrease in voluntary energy expenditure is governed by dietary macronutrient composition in mice.

In humans, exercise-induced thermogenesis is a markedly variable component of total energy expenditure, which had been acutely affected worldwide by COVID-19 pandemic-related lockdowns. We hypothesized that dietary macronutrient composition may affect metabolic adaptation/fuel selection in response to an acute decrease in voluntary activity. Using mice fed short-term high-fat diet (HFD) compared to low-fat diet (LFD)-fed mice, we evaluated whole-body fuel utilization by metabolic cages before and 3 days after omitting a voluntary running wheel in the cage. Short-term (24-48 h) HFD was sufficient to increase energy intake, fat oxidation, and decrease carbohydrate oxidation. Running wheel omission did not change energy intake, but resulted in a significant 50% decrease in total activity and a ~20% in energy expenditure in the active phase (night-time), compared to the period with wheel, irrespective of the dietary composition, resulting in significant weight gain. Yet, while in LFD wheel omission significantly decreased active phase fat oxidation, thereby trending to increase respiratory exchange ratio (RER), in HFD it diminished active phase carbohydrate oxidation. In conclusion, acute decrease in voluntary activity resulted in positive energy balance in mice on both diets, and decreased oxidation of the minor energy (macronutrient) fuel source, demonstrating that dietary macronutrient composition determines fuel utilization choices under conditions of acute changes in energetic demand.

CTRP12 ablation differentially affects energy expenditure, body weight, and insulin sensitivity in male and female mice.

Secreted hormones facilitate tissue cross talk to maintain energy balance. We previously described C1q/TNF-related protein 12 (CTRP12) as a novel metabolic hormone. Gain-of-function and partial-deficiency mouse models have highlighted important roles for this fat-derived adipokine in modulating systemic metabolism. Whether CTRP12 is essential and required for metabolic homeostasis is unknown. We show here that homozygous deletion of Ctrp12 gene results in sexually dimorphic phenotypes. Under basal conditions, complete loss of CTRP12 had little impact on male mice, whereas it decreased body weight (driven by reduced lean mass and liver weight) and improved insulin sensitivity in female mice. When challenged with a high-fat diet, Ctrp12 knockout (KO) male mice had decreased energy expenditure, increased weight gain and adiposity, elevated serum TNFα level, and reduced insulin sensitivity. In contrast, female KO mice had reduced weight gain and liver weight. The expression of lipid synthesis and catabolism genes, as well as profibrotic, endoplasmic reticulum stress, and oxidative stress genes were largely unaffected in the adipose tissue of Ctrp12 KO male mice. Despite greater adiposity and insulin resistance, Ctrp12 KO male mice fed an obesogenic diet had lower circulating triglyceride and free fatty acid levels. In contrast, lipid profiles of the leaner female KO mice were not different from those of WT controls. These data suggest that CTRP12 contributes to whole body energy metabolism in genotype-, diet-, and sex-dependent manners, underscoring complex gene-environment interactions influencing metabolic outcomes.

Classification of premalignant pancreatic cancer mass-spectrometry data using decision tree ensembles.

BACKGROUND: Pancreatic cancer is the fourth leading cause of cancer death in the United States. Consequently, identification of clinically relevant biomarkers for the early detection of this cancer type is urgently needed. In recent years, proteomics profiling techniques combined with various data analysis methods have been successfully used to gain critical insights into processes and mechanisms underlying pathologic conditions, particularly as they relate to cancer. However, the high dimensionality of proteomics data combined with their relatively small sample sizes poses a significant challenge to current data mining methodology where many of the standard methods cannot be applied directly. Here, we propose a novel methodological framework using machine learning method, in which decision tree based classifier ensembles coupled with feature selection methods, is applied to proteomics data generated from premalignant pancreatic cancer. RESULTS: This study explores the utility of three different feature selection schemas (Student t test, Wilcoxon rank sum test and genetic algorithm) to reduce the high dimensionality of a pancreatic cancer proteomic dataset. Using the top features selected from each method, we compared the prediction performances of a single decision tree algorithm C4.5 with six different decision-tree based classifier ensembles (Random forest, Stacked generalization, Bagging, Adaboost, Logitboost and Multiboost). We show that ensemble classifiers always outperform single decision tree classifier in having greater accuracies and smaller prediction errors when applied to a pancreatic cancer proteomics dataset. CONCLUSION: In our cross validation framework, classifier ensembles generally have better classification accuracies compared to that of a single decision tree when applied to a pancreatic cancer proteomic dataset, thus suggesting its utility in future proteomics data analysis. Additionally, the use of feature selection method allows us to select biomarkers with potentially important roles in cancer development, therefore highlighting the validity of this method.