Green tea, black tea, and epigallocatechin modify body composition, improve glucose tolerance, and differentially alter metabolic gene expression in rats fed a high-fat diet

The mechanisms of how tea and epigallocatechin-3-gallate (EGCG) lower body fat are not completely understood. This study investigated long-term administration of green tea (GT), black tea (BT), or isolated EGCG (1 mg/kg per day) on body composition, glucose tolerance, and gene expression related to energy metabolism and lipid homeostasis; it was hypothesized that all treatments would improve the indicators of metabolic syndrome. Rats were fed a 15% fat diet for 6 months from 4 weeks of age and were supplied GT, BT, EGCG, or water. GT and BT reduced body fat, whereas GT and EGCG increased lean mass. At 16 weeks GT, BT, and EGCG improved glucose tolerance. In the liver, GT and BT increased the expression of genes involved in fatty acid synthesis (SREBP-1c, FAS, MCD, ACC) and oxidation (PPAR-α, CPT-1, ACO); however, EGCG had no effect. In perirenal fat, genes that mediate adipocyte differentiation were suppressed by GT (Pref-1, C/EBP-β, and PPAR-γ) and BT (C/EBP-β), while decreasing LPL, HSL, and UCP-2 expression; EGCG increased expression of UCP-2 and PPAR-γ genes. Liver triacylglycerol content was unchanged. The results suggest that GT and BT suppressed adipocyte differentiation and fatty acid uptake into adipose tissue, while increasing fat synthesis and oxidation by the liver, without inducing hepatic fat accumulation. In contrast, EGCG increased markers of thermogenesis and differentiation in adipose tissue, while having no effect on liver or muscle tissues at this dose. These results show novel and separate mechanisms by which tea and EGCG may improve glucose tolerance and support a role for these compounds in obesity prevention.

Effect of carbohydrate ingestion on exercise-induced alterations in metabolic gene expression

Skeletal muscle possesses a high degree of plasticity and can adapt to both the physical and metabolic challenges that it faces. An acute bout of exercise is sufficient to induce the expression of a variety of metabolic genes, such as GLUT4, pyruvate dehydrogenase kinase 4 (PDK-4), uncoupling protein-3 (UCP3), and peroxisome proliferator-activated receptor-? coactivator 1 (PGC-1). Reducing muscle glycogen levels before exercise potentiates the effect of exercise on many genes. Similarly, altered substrate availability induces transcription of many of these genes. The purpose of this study was to determine whether glucose ingestion attenuates the exercise-induced increase in a variety of exercise-responsive genes. Six male subjects (28 ± 7 yr; 83 ± 3 kg; peak pulmonary oxygen uptake = 46 ± 6 ml·kg–1·min–1) performed 60 min of cycling at 74 ± 2% of peak pulmonary oxygen uptake on two separate occasions. On one occasion, subjects ingested a 6% carbohydrate drink. On the other occasion, subjects ingested an equal volume of a sweet placebo. Muscle samples were obtained from vastus lateralis at rest, immediately after exercise, and 3 h after exercise. PDK-4, UCP3, PGC-1, and GLUT4 mRNA levels were measured on these samples using real-time RT-PCR. Glucose ingestion attenuated (P < 0.05) the exercise-induced increase in PDK-4 and UCP3 mRNA. A similar trend (P = 0.09) was observed for GLUT4 mRNA. In contrast, PGC-1 mRNA increased following exercise to the same extent in both conditions. These data suggest that glucose availability can modulate the effect of exercise on metabolic gene expression.

Effects of vegetarianism and exercise on muscle creatine loading and gene expression

The results of this thesis indicate that vegetarianism and prior exercise can increase skeletal muscle creatine loading. The mechanisms by which these phenomena occur remain unknown but may be related to changes in the gene expression of a cellular pump within the muscle known as the "creatine transporter".

Finding rule groups to classify high dimensional gene expression datasets

Microarray data provides quantitative information about the transcription profile of cells. To analyse microarray datasets, methodology of machine learning has increasingly attracted bioinformatics researchers. Some approaches of machine learning are widely used to classify and mine biological datasets. However, many gene expression datasets are extremely high dimensionality, traditional machine learning methods cannot be applied effectively and efficiently. This paper proposes a robust algorithm to find out rule groups to classify gene expression datasets. Unlike the most classification algorithms, which select dimensions (genes) heuristically to form rules groups to identify classes such as cancerous and normal tissues, our algorithm guarantees finding out best-k dimensions (genes) to form rule groups for the classification of expression datasets. Our experiments show that the rule groups obtained by our algorithm have higher accuracy than that of other classification approaches.

A gene expression signature for insulin resistance

Insulin resistance is a heterogeneous disorder caused by a range of genetic and environmental factors, and we hypothesize that its aetiology varies considerably between individuals. This heterogeneity provides significant challenges to the development of effective therapeutic regimes for long-term management of type 2 diabetes. We describe a novel strategy, using large-scale gene expression profiling, to develop a Gene Expression Signature (GES) that reflects the overall state of insulin resistance in cells and patients. The GES was developed from 3T3-L1 adipocytes that were made ‘insulin resistant’ by treatment with tumour necrosis factor-alpha (TNFα) and then reversed with aspirin and troglitazone (‘re-sensitized’). The GES consisted of five genes whose expression levels best discriminated between the insulin resistant and insulin re-sensitized states. We then used this GES to screen a compound library for agents that affected the GES genes in 3T3- L1 adipocytes in a way that most closely resembled the changes seen when insulin resistance was successfully reversed using aspirin and troglitazone. This screen identified both known and new insulin sensitizing compounds including non-steroidal anti inflammatory agents, β-adrenergic antagonists, beta-lactams and sodium channel blockers. We tested the biological relevance of this GES in participants in the San Antonio Family Heart Study (n = 1,240) and showed that patients with the lowest GES scores were more insulin resistant (according to HOMA_IR and fasting plasma insulin levels, P < 0.001). These findings show that GES technology can be used for both the discovery of insulin sensitizing compounds and the characterization of patients into subtypes of insulin resistance according to GES scores, opening the possibility of developing a personalized medicine approach to type 2 diabetes.

Docosapentaenoic acid : its metabolism and effect on lipogenic gene expression

This thesis found that the omega-3 fatty acid, docosapentaenoic acid (DPA) down-regulates the expression levels of key lipogenic genes and proteins in vitro. In vivo studies with labelled DPA showed that, like docosahexaenoic acid, DPA is more conserved from oxidation compared with eicosapentaenoic acid.

Lactation related gene expression data

RNA sequencing and gene expression data related to lactation (mammary gland, milk and their sub compartments) obtained in a number of species (buffalo, mice, human, seal, wallaby, platypus).

Histological and global gene expression analysis of the ‘lactating’ pigeon crop

Background: Both male and female pigeons have the ability to produce a nutrient solution in their crop for the nourishment of their young. The production of the nutrient solution has been likened to lactation in mammals, and hence the product has been called pigeon ‘milk’. It has been shown that pigeon ‘milk’ is essential for growth and development of the pigeon squab, and without it they fail to thrive. Studies have investigated the nutritional value of pigeon ‘milk’ but very little else is known about what it is or how it is produced. This study aimed to gain insight into the process by studying gene expression in the ‘lactating’ crop.
Results: Macroscopic comparison of ‘lactating’ and non-’lactating’ crop reveals that the ‘lactating’ crop is enlarged and thickened with two very obvious lateral lobes that contain discrete rice-shaped pellets of pigeon ‘milk’. This was characterised histologically by an increase in the number and depth of rete pegs extending from the basal layer of the epithelium to the lamina propria, and extensive proliferation and folding of the germinal layer into the superficial epithelium. A global gene expression profile comparison between ‘lactating’ crop and non-’lactating’ crop showed that 542 genes are up-regulated in the ‘lactating’ crop, and 639 genes are down-regulated. Pathway analysis revealed that genes up-regulated in ‘lactating’ crop were involved in the proliferation of melanocytes, extracellular matrix-receptor interaction, the adherens junction and the wingless (wnt) signalling pathway. Gene ontology analysis showed that antioxidant response and microtubule transport were enriched in ‘lactating’ crop.
Conclusions: There is a hyperplastic response in the pigeon crop epithelium during ‘lactation’ that leads to localised cellular stress and expression of antioxidant protein-encoding genes. The differentiated, cornified cells that form the pigeon ‘milk’ are of keratinocyte lineage and contain triglycerides that are likely endocytosed as very low density lipoprotein (VLDL) and repackaged as triglyceride in vesicles that are transported intracellularly by microtubules. This mechanism is an interesting example of the evolution of a system with analogies to mammalian lactation, as pigeon ‘milk’ fulfils a similar function to mammalian milk, but is produced by a different mechanism.