Six weeks' sebacic acid supplementation improves fasting plasma glucose, HbA1c and glucose tolerance in db/db mice

Aim: To investigate the impact of chronic ingestion of sebacic acid (SA), a 10-carbon medium-chain dicarboxylic acid, on glycaemic control in a mouse model of type 2 diabetes (T2D)

Exploring the use of internal and externalcontrols for assessing microarray technical performance

<p>Abstract</p> <p>Background</p> <p>The maturing of gene expression microarray technology and interest in the use of microarray-based applications for clinical and diagnostic applications calls for quantitative measures of quality. This manuscript presents a retrospective study characterizing several approaches to assess technical performance of microarray data measured on the Affymetrix GeneChip platform, including whole-array metrics and information from a standard mixture of external spike-in and endogenous internal controls. Spike-in controls were found to carry the same information about technical performance as whole-array metrics and endogenous "housekeeping" genes. These results support the use of spike-in controls as general tools for performance assessment across time, experimenters and array batches, suggesting that they have potential for comparison of microarray data generated across species using different technologies.</p> <p>Results</p> <p>A layered PCA modeling methodology that uses data from a number of classes of controls (spike-in hybridization, spike-in polyA+, internal RNA degradation, endogenous or "housekeeping genes") was used for the assessment of microarray data quality. The controls provide information on multiple stages of the experimental protocol (e.g., hybridization, RNA amplification). External spike-in, hybridization and RNA labeling controls provide information related to both assay and hybridization performance whereas internal endogenous controls provide quality information on the biological sample. We find that the variance of the data generated from the external and internal controls carries critical information about technical performance; the PCA dissection of this variance is consistent with whole-array quality assessment based on a number of quality assurance/quality control (QA/QC) metrics.</p> <p>Conclusions</p> <p>These results provide support for the use of both external and internal RNA control data to assess the technical quality of microarray experiments. The observed consistency amongst the information carried by internal and external controls and whole-array quality measures offers promise for rationally-designed control standards for routine performance monitoring of multiplexed measurement platforms.</p

Variation in fiberoptic bead-based oligonucleotide microarrays: dispersion characteristics among hybridization and biological replicate samples

BACKGROUND: Gene expression microarray technology continues to evolve and its use has expanded into all areas of biology. However, the high dimensionality of the data makes analysis a difficult challenge. Evaluating measurements and estimating the significance of the observed differences among samples remain important issues that must be addressed for each technology platform. In this work we use a consecutive sampling method to characterize the dispersion patterns of data generated from Illumina fiberoptic bead-based oligonucleotide arrays. RESULTS: To describe general properties of the dispersion we used a linear function SD = a + bY(mean), approximating the standard deviation across arrays (Y(mean )is the mean expression of a given consecutive sample). First we examined three levels of variability: 1) same cell culture, same reverse transcription, duplicate hybridizations; 2) same cell culture, reverse transcription replicates; 3) parallel cultures. Each higher level is expected to introduce a new source of variability. We observed minor differences in the constant term: the mean values are 3.5, 3.1 and 3.5, respectively. However, the mean coefficient b increased from 0.045 to 0.147 and 0.133. We compared the coefficients derived from the consecutive sampling to those obtained from the standard deviation of individual gene expressions and found them in good agreement. In the second experiment samples we detected 11 genes with systematically different expressions between the experiment samples treated with glucose oxidase and controls and corroborated the selection using the Mann-Whitney and other tests. We also compared the consecutive sampling and coincidence method to t-test: the average percentage of consistency was above 80 for the former and below 50 for the latter. CONCLUSION: Our results indicate that the consecutive sampling method and standard deviation function provide a convenient description of the overall dispersion of Illumina arrays. We observed that the constant term of the standard deviation function is at average approximately the same for duplicate hybridization as for the assays with additional sources of variability. Furthermore, among the genes affected by glucose oxidase treatment we identified 6 genes in oxidative stress pathways and 5 genes involved in DNA repair. Finally, we noted that the consecutive sampling and coincidence test provide, under given conditions, more consistent results than the t-test. REVIEWERS: This article was reviewed by Alexander Karpikov (nominated by MarkGerstein), Jordan King and Eugene V. Koonin

Consequences of Exchanging Carbohydrates for Proteins in the Cholesterol Metabolism of Mice Fed a High-fat Diet

Consumption of low-carbohydrate, high-protein, high-fat diets lead to rapid weight loss but the cardioprotective effects of these diets have been questioned. We examined the impact of high-protein and high-fat diets on cholesterol metabolism by comparing the plasma cholesterol and the expression of cholesterol biosynthesis genes in the liver of mice fed a high-fat (HF) diet that has a high (H) or a low (L) protein-to-carbohydrate (P/C) ratio. H-P/C-HF feeding, compared with L-P/C-HF feeding, decreased plasma total cholesterol and increased HDL cholesterol concentrations at 4-wk. Interestingly, the expression of genes involved in hepatic steroid biosynthesis responded to an increased dietary P/C ratio by first down-regulation (2-d) followed by later up-regulation at 4-wk, and the temporal gene expression patterns were connected to the putative activity of SREBF1 and 2. In contrast, Cyp7a1, the gene responsible for the conversion of cholesterol to bile acids, was consistently up-regulated in the H-P/C-HF liver regardless of feeding duration. Over expression of Cyp7a1 after 2-d and 4-wk H-P/C-HF feeding was connected to two unique sets of transcription regulators. At both time points, up-regulation of the Cyp7a1 gene could be explained by enhanced activations and reduced suppressions of multiple transcription regulators. In conclusion, we demonstrated that the hypocholesterolemic effect of H-P/C-HF feeding coincided with orchestrated changes of gene expressions in lipid metabolic pathways in the liver of mice. Based on these results, we hypothesize that the cholesterol lowering effect of high-protein feeding is associated with enhanced bile acid production but clinical validation is warranted. (246 words

Probe Level Analysis of Affymetrix Microarray Data

The analysis of Affymetrix GeneChip® data is a complex, multistep process. Most often, methodscondense the multiple probe level intensities into single probeset level measures (such as RobustMulti-chip Average (RMA), dChip and Microarray Suite version 5.0 (MAS5)), which are thenfollowed by application of statistical tests to determine which genes are differentially expressed. An alternative approach is a probe-level analysis, which tests for differential expression directly using the probe-level data. Probe-level models offer the potential advantage of more accurately capturing sources of variation in microarray experiments. However, this has not been thoroughly investigated, since current research efforts have largely focused on the development of improved expression summary methods. This research project will review current approaches to analysis of probe-level data and discuss extensions of two examples, the S-Score and the Random Variance Model (RVM). The S-Score is a probe-level algorithm based on an error model in which the detected signal is proportional to the probe pair signal for highly expressed genes, but approaches a background level (rather than 0) for genes with low levels of expression. Initial results with the S-Score have been promising, but the method has been limited to two-chip comparisons. This project presents extensions to the S-Score that permit comparisons of multiple chips and borrowing of information across probes to increase statistical power. The RVM is a probeset-level algorithm that models the variance of the probeset intensities as a random sample from a common distribution to borrow information across genes. This project presents extensions to the RVM for probe-level data, using multivariate statistical theory to model the covariance among probes in a probeset. Both of these methods show the advantages of probe-level, rather than probeset-level, analysis in detecting differential gene expression for Afymetrix GeneChip data. Future research will focus on refining the probe-level models of both the S-Score and RVM algorithms to increase the sensitivity and specificity of microarray experiments