Mining Microarrays for Metabolic Meaning: Nutritional Regulation of Hypothalamic Gene Expression (original) (raw)
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Feeding induced changes in the hypothalamic transcriptome
Clinica Chimica Acta, 2009
Background: Obesity is a complex multifactorial disorder which needs a comprehensive approach for prevention and treatment. We investigated the modifications in the hypothalamic gene expression induced by high-fat (HF) and low-fat (LF) meal ingestion in mice, in order to identify the signals rapidly mediating the hypothalamic control on energy intake. Methods: After fasting, 1 group of mice was sacrificed and the others were fed ad libitum with HF or LF diet, and sacrificed 3 h after the beginning of the meal. The hypothalamus was sampled and the serial analysis of gene expression method was performed. Results: Approximately 254,588 tags, which correspond to 65,548 tag species were isolated from the 3 groups. The data showed twelve transcripts regulated by food intake. Among these, 2 transcripts have mitochondrial functions (MtCo1, Ppid), 3 are involved in protein transport and regulation (Ube2q2, Mup1, Sec13), 1 in cellular pH control (Slc4a3) and another 1 has a role in the epigenetic control of gene expression (Setd3). In addition, 5 potentially novel transcripts were differentially modulated. Conclusion: We identified genes that may regulate hypothalamic circuits governing the early response to food intake. 3 genes were specifically modulated by high-fat intake.
DNA Microarray Analysis of Genes Differentially Expressed in Diet-Induced (Cafeteria) Obese Rats
Obesity, 2003
Objective: To better understand the molecular basis of dietary obesity, we examined adipose tissue genes differentially expressed in an obesity model using DNA microarray analysis.Research Methods and Procedures: We assessed the expression level of over 12, 500 transcripts in epididymal fat pads from (cafeteria) obese and control rats with the aid of the array technology.Results: Cafeteria (obese) rats weighed 50% more and had 2.5-fold higher levels of epididymal fat and elevated levels of circulating leptin. Adipose genes differentially expressed in obese and control rats were categorized into five groups: macronutrient metabolism, transcription factors, hormone receptor and signal transduction, redox and stress proteins, and cellular cytoskeleton. Interestingly, the expression levels of a number of genes involved in lipid metabolism such as glycerol-3-phosphate dehydrogenase, stearoyl coenzyme A desaturase, together with the transcription factors implicated in adipocyte differentiation (CAAT/enhancer binding protein-α and peroxisome proliferator-activated receptor-γ), were significantly increased in obese animals compared with control. The most up-regulated transcripts were the ob (49.2-fold change) and the fatty acid-binding protein genes (15.7- fold change). In contrast, genes related to redox and stress protein were generally down-regulated in obese animals compared with the control.Discussion: Our study showed that in diet-induced obesity, the expression levels of some important genes implicated in lipid metabolism were up-regulated, whereas those related to redox and stress protein were down-regulated in obese animals compared with control. This pattern of gene expression may occur in human obesity cases after high-fat intake.
2012
The hypothalamus is an essential relay in the neural circuitry underlying energy metabolism that needs to continually adapt to changes in the energetic environment. The neuroendocrine control of food intake and energy expenditure is associated with, and likely dependent upon, hypothalamic plasticity. Severe disturbances in energy metabolism, such as those that occur in obesity, are therefore likely to be associated with disruption of hypothalamic transcriptomic plasticity. In this paper, we investigated the effects of two well-characterized antiaging interventions, caloric restriction and voluntary wheel running, in two distinct physiological paradigms, that is, diabetic (db/db) and nondiabetic wild-type (C57/Bl/6) animals to investigate the contextual sensitivity of hypothalamic transcriptomic responses. We found that, both quantitatively and qualitatively, caloric restriction and physical exercise were associated with distinct transcriptional signatures that differed significantly between diabetic and nondiabetic mice. This suggests that challenges to metabolic homeostasis regulate distinct hypothalamic gene sets in diabetic and non-diabetic animals. A greater understanding of how genetic background contributes to hypothalamic response mechanisms could pave the way for the development of more nuanced therapeutics for the treatment of metabolic disorders that occur in diverse physiological backgrounds.
Journal of Neural Transmission, 2006
Atypical neuroleptics are associated with clinical significant weight gain, whereas stimulants are used as anorexiant drugs. The aim of this study was to examine gene expression changes in the mouse frontal cortex following chronic oral treatment with antipsychotics and a stimulant by microarray assessments. Twenty 10-12-week-old male C57BL6 mice received daily for 31 days either the typical neuroleptic haloperidol (1 mg=kg), the atypical neuroleptic clozapine (10 mg=kg) or the stimulant phenylpropanolamine (3 mg=kg). We identified a set of genes that was differently expressed between the neuroleptic-treated groups and the stimulant-treated group. Importantly, we found in the majority of gene alterations down-regulation in genes involved in ATP biosynthesis and lipid metabolism following the stimulant treatment, suggesting these genes as candidates that may regulate body weight. We also identified remarkable expression patterns of genes that encode signalling molecules (e.g. insulin, mitochondrial uncoupling protein 1) that are implicated in the control of food intake and are differently expressed in the neuroleptic groups.
Subtyping obesity with microarrays: implications for the diagnosis and treatment of obesity
International Journal of Obesity, 2009
Objective-Obese patients respond differently to weight loss interventions. No efficient diagnostic tool exists to separate obese patients into subtypes as a means to improve prediction of response to interventions. We aimed to separate obese subjects into distinct subgroups using microarray technology to identify gene expression-based subgroups to predict weight loss. Design-A total of 72 obese men and women without family history of diabetes were enrolled in the study; 52 were treated with ephedra and caffeine (E+C) and 20 with placebo for 8 weeks. Adipose and skeletal muscle tissue biopsies were performed at baseline. RNA sample pairs were labeled and hybridized to oligonucleotide microarrays. Quantile normalization and gene shaving were performed, and a clustering algorithm was then applied to cluster subjects based on their gene expression profile. Clusters were visualized using heat maps and related to weight changes. Results-Cluster analysis of gene expression data revealed two distinct subgroups of obesity and predicted weight loss in response to the treatment with E+C. One cluster (`red') decreased to 96.87 ±2.35% body weight, and the second cluster (`green') decreased to 95.59±2.75% body weight (P<0.05). `Red' cluster had less visceral adipose tissue mass (2.77±1.08 vs 3.43±1.49 kg; P<0.05) and decreased size of the very large fat cells (1.45±0.61 vs 2.16±1.74 μl; P<0.05) compared to `green' cluster. Gene expression for both skeletal muscle and adipose tissue was also different between clusters. Conclusions-Our study provides the first evidence that the combined approach of gene expression profiling and cluster analysis can identify discrete subtypes of obesity, these subtypes have different physiological characteristics and respond differently to an adrenergic weight loss therapy. This brings us that into an era of personalized treatment in the obesity clinic.
PLoS Computational Biology, 2010
Although they have become a widely used experimental technique for identifying differentially expressed (DE) genes, DNA microarrays are notorious for generating noisy data. A common strategy for mitigating the effects of noise is to perform many experimental replicates. This approach is often costly and sometimes impossible given limited resources; thus, analytical methods are needed which increase accuracy at no additional cost. One inexpensive source of microarray replicates comes from prior work: to date, data from hundreds of thousands of microarray experiments are in the public domain. Although these data assay a wide range of conditions, they cannot be used directly to inform any particular experiment and are thus ignored by most DE gene methods. We present the SVD Augmented Gene expression Analysis Tool (SAGAT), a mathematically principled, data-driven approach for identifying DE genes. SAGAT increases the power of a microarray experiment by using observed coexpression relationships from publicly available microarray datasets to reduce uncertainty in individual genes' expression measurements. We tested the method on three well-replicated human microarray datasets and demonstrate that use of SAGAT increased effective sample sizes by as many as 2.72 arrays. We applied SAGAT to unpublished data from a microarray study investigating transcriptional responses to insulin resistance, resulting in a 50% increase in the number of significant genes detected. We evaluated 11 (58%) of these genes experimentally using qPCR, confirming the directions of expression change for all 11 and statistical significance for three. Use of SAGAT revealed coherent biological changes in three pathways: inflammation, differentiation, and fatty acid synthesis, furthering our molecular understanding of a type 2 diabetes risk factor. We envision SAGAT as a means to maximize the potential for biological discovery from subtle transcriptional responses, and we provide it as a freely available software package that is immediately applicable to any human microarray study.
Acute Induction of Gene Expression in Brain and Liver by Insulin-Induced Hypoglycemia
Diabetes, 2005
The robust neuroendocrine counterregulatory responses induced by hypoglycemia protect the brain by restoring plasma glucose, but little is known about molecular responses to hypoglycemia that may also be neuroprotective. To clarify these mechanisms, we examined gene expression in hypothalamus, cortex, and liver 3 h after induction of mild hypoglycemia by a single injection of insulin, using cDNA microarray analysis and quantitative real-time PCR. Real-time PCR corroborated the induction of six genes (angiotensinogen, GLUT-1, inhibitor of B, inhibitor of DNA binding 1 [ID-1], Ubp41, and mitogen-activated protein kinase phosphatase-1 [MKP-1]) by insulin-induced hypoglycemia in the hypothalamus: five of these six genes in cortex and three (GLUT-1, angiotensinogen, and MKP-1) in liver. The induction was due to hypoglycemia and not hyperinsulinemia, since fasting (characterized by low insulin and glucose) also induced these genes. Four of these genes (angiotensinogen, GLUT-1, ID-1, and MKP-1) have been implicated in enhancement of glucose availability, which could plausibly serve a neuroprotective role during acute hypoglycemia but, if persistent, could also cause glucose-sensing mechanisms to overestimate plasma glucose levels, potentially causing hypoglycemia-induced counterregulatory failure. Although using cDNA microarrays with more genes, or microdissection, would presumably reveal further responses to hypoglycemia, these hypoglycemia-induced genes represent useful markers to assess molecular mechanisms mediating cellular responses to hypoglycemia. Diabetes 54:952-958, 2005