Novel Indirect Calorimetry Technology to Analyze Metabolism in Individual Neonatal Rodent Pups

BACKGROUND: The ability to characterize the development of metabolic function in neonatal rodents has been limited due to technological constraints. Low respiratory volumes and flows at rest pose unique problems, making it difficult to reliably measure O(2) consumption, CO(2) production, respiratory quotient (RQ), and energy expenditure (EE). Our aim was to develop and validate a commercial-grade indirect calorimetry system capable of characterizing the metabolic phenotype of individual neonatal rodents. METHODOLOGY/PRINCIPAL FINDINGS: To address this research need, we developed a novel, highly sensitive open-circuit indirect calorimetry system capable of analyzing respiratory gas exchange in a single neonatal rodent pup. Additionally, we derived an equation from known metabolic relationships to estimate inlet flow rates, improving the efficiency of data collection. To validate the neonatal rodent indirect calorimetry system and evaluate the applicability of the derived equation for predicting appropriate flow rates, we conducted a series of experiments evaluating the impact of sex, litter size, time of day (during the light phase), and ambient temperature on neonatal rat metabolic parameters. Data revealed that the only metabolic parameter influenced by litter size is a neonatal rat's RQ, with rat pups reared in a small litter (5 pups) having lower RQ's than rat pups reared in either medium (8 pups) or large (11 pups) litters. Furthermore, data showed that ambient temperature affected all metabolic parameters measured, with colder temperatures being associated with higher CO(2) production, higher O(2) consumption, and higher energy expenditure. CONCLUSION/SIGNIFICANCE: The results of this study demonstrate that the modified Panlab Oxylet system reliably assesses early postnatal metabolism in individual neonatal rodents. This system will be of paramount importance to further our understanding of processes associated with the developmental origins of adult metabolic disease

Litter Size Variation in Hypothalamic Gene Expression Determines Adult Metabolic Phenotype in Brandt's Voles (Lasiopodomys brandtii)

Early postnatal environments may have long-term and potentially irreversible consequences on hypothalamic neurons involved in energy homeostasis. Litter size is an important life history trait and negatively correlated with milk intake in small mammals, and thus has been regarded as a naturally varying feature of the early developmental environment. Here we investigated the long-term effects of litter size on metabolic phenotype and hypothalamic neuropeptide mRNA expression involved in the regulation of energy homeostasis, using the offspring reared from large (10-12) and small (3-4) litter sizes, of Brandt's voles (Lasiopodomys brandtii), a rodent species from Inner Mongolia grassland in China.Hypothalamic leptin signaling and neuropeptides were measured by Real-Time PCR. We showed that offspring reared from small litters were heavier at weaning and also in adulthood than offspring from large litters, accompanied by increased food intake during development. There were no significant differences in serum leptin levels or leptin receptor (OB-Rb) mRNA in the hypothalamus at weaning or in adulthood, however, hypothalamic suppressor of cytokine signaling 3 (SOCS3) mRNA in adulthood increased in small litters compared to that in large litters. As a result, the agouti-related peptide (AgRP) mRNA increased in the offspring from small litters.These findings support our hypothesis that natural litter size has a permanent effect on offspring metabolic phenotype and hypothalamic neuropeptide expression, and suggest central leptin resistance and the resultant increase in AgRP expression may be a fundamental mechanism underlying hyperphagia and the increased risk of overweight in pups of small litters. Thus, we conclude that litter size may be an important and central determinant of metabolic fitness in adulthood

Myogenin Regulates Exercise Capacity and Skeletal Muscle Metabolism in the Adult Mouse

Although skeletal muscle metabolism is a well-studied physiological process, little is known about how it is regulated at the transcriptional level. The myogenic transcription factor myogenin is required for skeletal muscle development during embryonic and fetal life, but myogenin's role in adult skeletal muscle is unclear. We sought to determine myogenin's function in adult muscle metabolism. A Myog conditional allele and Cre-ER transgene were used to delete Myog in adult mice. Mice were analyzed for exercise capacity by involuntary treadmill running. To assess oxidative and glycolytic metabolism, we performed indirect calorimetry, monitored blood glucose and lactate levels, and performed histochemical analyses on muscle fibers. Surprisingly, we found that Myog-deleted mice performed significantly better than controls in high- and low-intensity treadmill running. This enhanced exercise capacity was due to more efficient oxidative metabolism during low- and high-intensity exercise and more efficient glycolytic metabolism during high-intensity exercise. Furthermore, Myog-deleted mice had an enhanced response to long-term voluntary exercise training on running wheels. We identified several candidate genes whose expression was altered in exercise-stressed muscle of mice lacking myogenin. The results suggest that myogenin plays a critical role as a high-level transcriptional regulator to control the energy balance between aerobic and anaerobic metabolism in adult skeletal muscle

Anthropometry‐based prediction of body composition in early infancy compared to air‐displacement plethysmography

Funder: Danone Nutricia ResearchFunder: EU Commission for JPI HDHL program ‘Call III Biomarkers’ for project: BioFN ‐ Biomarkers for Infant Fat Mass Development and Nutrition; Grant(s): 696295Summary: Background: Anthropometry‐based equations are commonly used to estimate infant body composition. However, existing equations were designed for newborns or adolescents. We aimed to (a) derive new prediction equations in infancy against air‐displacement plethysmography (ADP‐PEA Pod) as the criterion, (b) validate the newly developed equations in an independent infant cohort and (c) compare them with published equations (Slaughter‐1988, Aris‐2013, Catalano‐1995). Methods: Cambridge Baby Growth Study (CBGS), UK, had anthropometry data at 6 weeks (N = 55) and 3 months (N = 64), including skinfold thicknesses (SFT) at four sites (triceps, subscapular, quadriceps and flank) and ADP‐derived total body fat mass (FM) and fat‐free mass (FFM). Prediction equations for FM and FFM were developed in CBGS using linear regression models and were validated in Sophia Pluto cohort, the Netherlands, (N = 571 and N = 447 aged 3 and 6 months, respectively) using Bland–Altman analyses to assess bias and 95% limits of agreement (LOA). Results: CBGS equations consisted of sex, age, weight, length and SFT from three sites and explained 65% of the variance in FM and 79% in FFM. In Sophia Pluto, these equations showed smaller mean bias than the three published equations in estimating FM: mean bias (LOA) 0.008 (−0.489, 0.505) kg at 3 months and 0.084 (−0.545, 0.713) kg at 6 months. Mean bias in estimating FFM was 0.099 (−0.394, 0.592) kg at 3 months and −0.021 (−0.663, 0.621) kg at 6 months. Conclusions: CBGS prediction equations for infant FM and FFM showed better validity in an independent cohort at ages 3 and 6 months than existing equations