Genotyping Performance between Saliva and Blood-Derived Genomic DNAs on the DMET Array: A Comparison

The Affymetrix Drug Metabolism Enzymes and Transporters (DMET) microarray is the first assay to offer a large representation of SNPs conferring genetic diversity across known pharmacokinetic markers. As a convenient and painless alternative to blood, saliva samples have been reported to work well for genotyping on the high density SNP arrays, but no reports to date have examined this application for saliva-derived DNA on the DMET platform. Genomic DNA extractions from saliva samples produced an ample quantity of genomic DNA for DMET arrays, however when human amplifiable DNA was measured, it was determined that a large percentage of this DNA was from bacteria or fungi. A mean of 37.3% human amplifiable DNA was determined for saliva-derived DNAs, which results in a significant decrease in the genotyping call rate (88.8%) when compared with blood-derived DNAs (99.1%). More interestingly, the percentage of human amplifiable DNA correlated with a higher genotyping call rate, and almost all samples with more than 31.3% human DNA produced a genotyping call rate of at least 96%. SNP genotyping results for saliva derived DNA (n = 39) illustrated a 98.7% concordance when compared with blood DNA. In conclusion, when compared with blood DNA and tested on the DMET array, saliva-derived DNA provided adequate genotyping quality with a significant lower number of SNP calls. Saliva-derived DNA does perform very well if it contains greater than 31.3% human amplifiable DNA

Comparison of Enhanced Noise Model Performance Based on Analysis of Civilian GPS Data

We recorded the time series of location data from stationary, single-frequency (L1) GPS positioning systems at a variety of geographic locations. The empirical autocorrelation function of these data shows significant temporal correlations. The Gaussian white noise model, widely used in sensor-fusion algorithms, does not account for the observed autocorrelations and has an artificially large variance. Noise-model analysis—using Akaike’s Information Criterion—favours alternative models, such as an Ornstein–Uhlenbeck or an autoregressive process. We suggest that incorporating a suitable enhanced noise model into applications (e.g., Kalman Filters) that rely on GPS position estimates will improve performance. This provides an alternative to explicitly modelling possible sources of correlation (e.g., multipath, shadowing, or other second-order physical phenomena)

Metformin and berberine prevent olanzapine-induced weight gain in rats.

Olanzapine is a first line medication for the treatment of schizophrenia, but it is also one of the atypical antipsychotics carrying the highest risk of weight gain. Metformin was reported to produce significant attenuation of antipsychotic-induced weight gain in patients, while the study of preventing olanzapine-induced weight gain in an animal model is absent. Berberine, an herbal alkaloid, was shown in our previous studies to prevent fat accumulation in vitro and in vivo. Utilizing a well-replicated rat model of olanzapine-induced weight gain, here we demonstrated that two weeks of metformin or berberine treatment significantly prevented the olanzapine-induced weight gain and white fat accumulation. Neither metformin nor berberine treatment demonstrated a significant inhibition of olanzapine-increased food intake. But interestingly, a significant loss of brown adipose tissue caused by olanzapine treatment was prevented by the addition of metformin or berberine. Our gene expression analysis also demonstrated that the weight gain prevention efficacy of metformin or berberine treatment was associated with changes in the expression of multiple key genes controlling energy expenditure. This study not only demonstrates a significant preventive efficacy of metformin and berberine treatment on olanzapine-induced weight gain in rats, but also suggests a potential mechanism of action for preventing olanzapine-reduced energy expenditure

Average weight gain, food intake, white fat weight, brown fat weight, and liver weight in different treatment group (N = 12).

<p>Average weight gain, food intake, white fat weight, brown fat weight, and liver weight in different treatment group (N = 12).</p

Average blood glucose, triglyceride, and total cholesterol levels (mg/dl) in non-fasted female SD rats (N = 12).

<p>Average blood glucose, triglyceride, and total cholesterol levels (mg/dl) in non-fasted female SD rats (N = 12).</p

Rat body weight gained during treatment (vehicle, olanzapine, olanzapine + berberine, olanzapine + metformin) (A).

<p>* denotes significant difference between olanzapine and control group at P<0.05; # denotes significant difference between olanzapine + berberine and olanzapine group at P<0.05; † denotes significant difference between olanzapine + metformin and olanzapine group at P<0.05. (B) Average food intake (g/rat/day) for each treatment group. (C) Rat white fat, brown fat, and liver weight percentage normalized to body weight in eacg treatment group. * denotes significant difference at P<0.05.</p

SNP primer-probe sets ordered from Life Technologies.

<p>SNP primer-probe sets ordered from Life Technologies.</p

Comparison of amplifiable human DNA percentage and genotyping call rate between blood and saliva derived DNA.

<p>Comparison of amplifiable human DNA percentage and genotyping call rate between blood and saliva derived DNA.</p

DMET Genotyping call rate (%) following the increasein the concentration of saliva-derived DNA.

<p>DMET Genotyping call rate (%) following the increasein the concentration of saliva-derived DNA.</p