Significance analysis of microarray transcript levels in time series experiments

Background: Microarray time series studies are essential to understand the dynamics of molecular events. In order to limit the analysis to those genes that change expression over time, a first necessary step is to select differentially expressed transcripts. A variety of methods have been proposed to this purpose; however, these methods are seldom applicable in practice since they require a large number of replicates, often available only for a limited number of samples. In this data-poor context, we evaluate the performance of three selection methods, using synthetic data, over a range of experimental conditions. Application to real data is also discussed. Results: Three methods are considered, to assess differentially expressed genes in data-poor conditions. Method 1 uses a threshold on individual samples based on a model of the experimental error. Method 2 calculates the area of the region bounded by the time series expression profiles, and considers the gene differentially expressed if the area exceeds a threshold based on a model of the experimental error. These two methods are compared to Method 3, recently proposed in the literature, which exploits splines fit to compare time series profiles. Application of the three methods to synthetic data indicates that Method 2 outperforms the other two both in Precision and Recall when short time series are analyzed, while Method 3 outperforms the other two for long time series. Conclusion: These results help to address the choice of the algorithm to be used in data-poor time series expression study, depending on the length of the time series

A quantization method based on threshold optimization for microarray short time series

BACKGROUND: Reconstructing regulatory networks from gene expression profiles is a challenging problem of functional genomics. In microarray studies the number of samples is often very limited compared to the number of genes, thus the use of discrete data may help reducing the probability of finding random associations between genes. RESULTS: A quantization method, based on a model of the experimental error and on a significance level able to compromise between false positive and false negative classifications, is presented, which can be used as a preliminary step in discrete reverse engineering methods. The method is tested on continuous synthetic data with two discrete reverse engineering methods: Reveal and Dynamic Bayesian Networks. CONCLUSION: The quantization method, evaluated in comparison with two standard methods, 5% threshold based on experimental error and rank sorting, improves the ability of Reveal and Dynamic Bayesian Networks to identify relations among genes

Metabolomics Workbench: An International Repository for Metabolomics Data and Metadata, Metabolite Standards, Protocols, Tutorials and Training, and Analysis Tools

The Metabolomics Workbench, available at www.metabolomicsworkbench.org, is a public repository for metabolomics metadata and experimental data spanning various species and experimental platforms, metabolite standards, metabolite structures, protocols, tutorials, and training material and other educational resources. It provides a computational platform to integrate, analyze, track, deposit and disseminate large volumes of heterogeneous data from a wide variety of metabolomics studies including mass spectrometry (MS) and nuclear magnetic resonance spectrometry (NMR) data spanning over 20 different species covering all the major taxonomic categories including humans and other mammals, plants, insects, invertebrates and microorganisms. Additionally, a number of protocols are provided for a range of metabolite classes, sample types, and both MS and NMR-based studies, along with a metabolite structure database. The metabolites characterized in the studies available on the Metabolomics Workbench are linked to chemical structures in the metabolite structure database to facilitate comparative analysis across studies. The Metabolomics Workbench, part of the data coordinating effort of the National Institute of Health (NIH) Common Fund\u27s Metabolomics Program, provides data from the Common Fund\u27s Metabolomics Resource Cores, metabolite standards, and analysis tools to the wider metabolomics community and seeks data depositions from metabolomics researchers across the world

Quantitative Metabolomics by 1H-NMR and LC-MS/MS Confirms Altered Metabolic Pathways in Diabetes

Insulin is as a major postprandial hormone with profound effects on carbohydrate, fat, and protein metabolism. In the absence of exogenous insulin, patients with type 1 diabetes exhibit a variety of metabolic abnormalities including hyperglycemia, glycosurea, accelerated ketogenesis, and muscle wasting due to increased proteolysis. We analyzed plasma from type 1 diabetic (T1D) humans during insulin treatment (I+) and acute insulin deprivation (I-) and non-diabetic participants (ND) by 1H nuclear magnetic resonance spectroscopy and liquid chromatography-tandem mass spectrometry. The aim was to determine if this combination of analytical methods could provide information on metabolic pathways known to be altered by insulin deficiency. Multivariate statistics differentiated proton spectra from I- and I+ based on several derived plasma metabolites that were elevated during insulin deprivation (lactate, acetate, allantoin, ketones). Mass spectrometry revealed significant perturbations in levels of plasma amino acids and amino acid metabolites during insulin deprivation. Further analysis of metabolite levels measured by the two analytical techniques indicates several known metabolic pathways that are perturbed in T1D (I-) (protein synthesis and breakdown, gluconeogenesis, ketogenesis, amino acid oxidation, mitochondrial bioenergetics, and oxidative stress). This work demonstrates the promise of combining multiple analytical methods with advanced statistical methods in quantitative metabolomics research, which we have applied to the clinical situation of acute insulin deprivation in T1D to reflect the numerous metabolic pathways known to be affected by insulin deficiency

Effect of Testosterone on Insulin Stimulated IRS1 Ser Phosphorylation in Primary Rat Myotubes—A Potential Model for PCOS-Related Insulin Resistance

Polycystic ovary syndrome (PCOS) is characterized by a hyperandrogenic state and frequently develops skeletal muscle insulin resistance. We determined whether testosterone adversely affects insulin action by increasing serine phosphorylation of IRS-1(636/639) in differentiated rat skeletal muscle myotubes. The phosphorylation of Akt, mTOR, and S6K, downstream targets of the PI3-kinase-IRS-1 complex were also studied.Primary differentiated rat skeletal muscle myotubes were subjected to insulin for 30 min after 16-hour pre-exposure to either low (20 ng/ml) or high (200 ng/ml) doses of testosterone. Protein phosphorylation of IRS-1 Ser(636/639), Akt Ser(473), mTOR-Ser(2448), and S6K-Thr(389) were measured by Western blot with signal intensity measured by immunofluorescence.Cells exposed to 100 nM of insulin had increased IRS-1 Ser(636/639) and Akt Ser(473) phosphorylation. Cells pre-exposed to low-dose testosterone had significantly increased insulin-induced mTOR-Ser(2448) and S6K-Thr(389) phosphorylation (p<0.05), and further increased insulin-induced IRS-1 Ser(636/639) phosphorylation (p = 0.042) compared to control cells. High-dose testosterone pre-exposure attenuated the insulin-induced mTOR-Ser(2448) and S6K-Thr(389) phosphorylation.The data demonstrated an interaction between testosterone and insulin on phosphorylation of intracellular signaling proteins, and suggests a link between a hyperandrogenic, hyperinsulinemic environment and the development of insulin resistance involving serine phosphorylation of IRS-1 Ser(636/639). These results may guide further investigations of potential mechanisms of PCOS-related insulin resistance

GDF15 mediates the effects of metformin on body weight and energy balance.

Metformin, the world's most prescribed anti-diabetic drug, is also effective in preventing type 2 diabetes in people at high risk1,2. More than 60% of this effect is attributable to the ability of metformin to lower body weight in a sustained manner3. The molecular mechanisms by which metformin lowers body weight are unknown. Here we show-in two independent randomized controlled clinical trials-that metformin increases circulating levels of the peptide hormone growth/differentiation factor 15 (GDF15), which has been shown to reduce food intake and lower body weight through a brain-stem-restricted receptor. In wild-type mice, oral metformin increased circulating GDF15, with GDF15 expression increasing predominantly in the distal intestine and the kidney. Metformin prevented weight gain in response to a high-fat diet in wild-type mice but not in mice lacking GDF15 or its receptor GDNF family receptor α-like (GFRAL). In obese mice on a high-fat diet, the effects of metformin to reduce body weight were reversed by a GFRAL-antagonist antibody. Metformin had effects on both energy intake and energy expenditure that were dependent on GDF15, but retained its ability to lower circulating glucose levels in the absence of GDF15 activity. In summary, metformin elevates circulating levels of GDF15, which is necessary to obtain its beneficial effects on energy balance and body weight, major contributors to its action as a chemopreventive agent

The Effect of High Glucocorticoid Administration and Food Restriction on Rodent Skeletal Muscle Mitochondrial Function and Protein Metabolism

Glucocorticoids levels are high in catabolic conditions but it is unclear how much of the catabolic effects are due to negative energy balance versus glucocorticoids and whether there are distinct effects on metabolism and functions of specific muscle proteins.We determined whether 14 days of high dose methylprednisolone (MPred, 4 mg/kg/d) Vs food restriction (FR, food intake matched to MPred) in rats had different effects on muscle mitochondrial function and protein fractional synthesis rates (FSR). Lower weight loss (15%) occurred in FR than in MPred (30%) rats, while a 15% increase occurred saline-treated Controls. The per cent muscle loss was significantly greater for MPred than FR. Mitochondrial protein FSR in MPred rats was lower in soleus (51 and 43%, respectively) and plantaris (25 and 55%) than in FR, while similar decline in protein FSR of the mixed, sarcoplasmic, and myosin heavy chain occurred. Mitochondrial enzymatic activity and ATP production were unchanged in soleus while in plantaris cytochrome c oxidase activity was lower in FR than Control, and ATP production rate with pyruvate + malate in MPred plantaris was 28% lower in MPred. Branched-chain amino acid catabolic enzyme activities were higher in both FR and MPred rats indicating enhanced amino acid oxidation capacity.MPred and FR had little impact on mitochondrial function but reduction in muscle protein synthesis occurred in MPred that could be explained on the basis of reduced food intake. A greater decline in proteolysis may explain lesser muscle loss in FR than in MPred rats

Function-Based Discovery of Significant Transcriptional Temporal Patterns in Insulin Stimulated Muscle Cells

Background: Insulin action on protein synthesis (translation of transcripts) and post-translational modifications, especially of those involving the reversible modifications such as phosphorylation of various signaling proteins, are extensively studied but insulin effect on transcription of genes, especially of transcriptional temporal patterns remains to be fully defined. Methodology/Principal Findings: To identify significant transcriptional temporal patterns we utilized primary differentiated rat skeletal muscle myotubes which were treated with insulin and samples were collected every 20 min for 8 hours. Pooled samples at every hour were analyzed by gene array approach to measure transcript levels. The patterns of transcript levels were analyzed based on a novel method that integrates selection, clustering, and functional annotation to find the main temporal patterns associated to functional groups of differentially expressed genes. 326 genes were found to be differentially expressed in response to in vitro insulin administration in skeletal muscle myotubes. Approximately 20 % of the genes that were differentially expressed were identified as belonging to the insulin signaling pathway. Characteristic transcriptional temporal patterns include: (a) a slow and gradual decrease in gene expression, (b) a gradual increase in gene expression reaching a peak at about 5 hours and then reaching a plateau or an initial decrease and other different variable pattern of increase in gene expression over time. Conclusion/Significance: The new method allows identifying characteristic dynamic responses to insulin stimulus, commo