The aim of this study was to test the hypothesis that hepatic vitamin C (VC) levels in VC deficient mice rescued with high doses of VC supplements still do not reach the optimal levels present in wild-type mice. For this, we used a mouse scurvy model (sfx) in which the L-gulonolactone oxidase gene (Gulo) is deleted. Six age- (6 weeks old) and gender- (female) matched wild-type (WT) and sfx mice (rescued by administering 500 mg of VC/L) were used as the control (WT) and treatment (MT) groups (n = 3 for each group), respectively. Total hepatic RNA was used in triplicate microarray assays for each group. EDGE software was used to identify differentially expressed genes and transcriptomic analysis was used to assess the potential genetic regulation of Gulo gene expression. Hepatic VC concentrations in MT mice were significantly lower than in WT mice, even though there were no morphological differences between the two groups. In MT mice, 269 differentially expressed transcripts were detected (> twice the difference between MT and WT mice), including 107 up-regulated and 162 down-regulated genes. These differentially expressed genes included stress-related and exclusively/predominantly hepatocyte genes. Transcriptomic analysis identified a major locus on chromosome 18 that regulates Gulo expression. Since three relevant oxidative genes are located within the critical region of this locus we suspect that they are involved in the down-regulation of oxidative activity in sfx mice.

Humans and guinea pigs are species which are unable to synthesize ascorbic acid (vitamin C) because, unlike rodents, they lack the enzyme L-gulonolactone oxidase (Gulo). Although the phenotype of lacking vitamin C in humans, named scurvy, has long been well known, information on the impact of lacking Gulo on the gene expression profiles of different tissues is still missing. This knowledge could improve our understanding of molecular pathways in which Gulo may be involved. Recently, we discovered a deletion that includes all 12 exons in the gene for Gulo in the sfx mouse, characterized by spontaneous bone fractures. We report here the initial analysis of the impact of the Gulo gene deletion on the murine gene expression profiles in the liver, femur and kidney.

The use of a constant fold-change to determine significant changes in gene expression has been widely accepted for its intuition and ease of use in microarray data analysis, but this concept has been increasingly criticized because it does not reflect signal intensity and can result in a substantial number of false positives and false negatives. To resolve this dilemma, we have analyzed 65 replicate Affymetrix chip-chip comparisons and determined a series of user adjustable signal-dependent thresholds which do not require replicates and offer a 95% confidence interval. Quantitative RT-PCR shows that such thresholds significantly improve the power to discriminate biological changes in mRNA from noise and reduce false calls compared to the traditional two-fold threshold. The user-friendly nature of this approach means that it can be easily applied by any user of microarray analysis, even those without any specialized knowledge of computational techniques or statistics. Noise is a function of signal intensity not only for Affymetrix data but also for cDNA array data, analysis of which may also be benefited by our methodology.