**6. Gene expression profiles change with other stresses**

Furthermore, a possibility was shown that whole blood RNA analysis is applicable to evaluation of physiological state.

Whole Blood RNA Analysis, Aging and Disease 115

can be a critical step in blood RNA assay [31]. Although PBMCs do not contain neutrophils, eosinophils, basophils, nor platelets, Min et al. reported highly correlated results (r2 = 0.85) for 8,273 genes expressed between the whole blood RNA, by using the PAX gene Blood RNA system, and peripheral blood mononuclear cell (PBMC) RNA samples isolated from healthy volunteers by using a Ficoll-Paque gradient and TRI Reagent (SIGMA) [32]. Other workers conducted a large scale genome-wide expression analysis of white blood cells subpopulations. This study indicates that correlation coefficients for T-cells and monocytes among different healthy subjects were 0.98±0.01 and 0.97±0.01, respectively. However, for the same subjects (n=5), correlation coefficients between T-cells and monocytes was 0.88±0.01, indicating varied correlation between white blood cells subpopulations. In addition, gene expression analysis were showed a varying dependence on the isolation method such as PAXgene, Buffy coat, and lysis. The correlation coefficients between isolation methods were 0.89±0.04, 0.91±0.04, 0.96±0.06, for PAXgene vs. lysis, PAXgene vs. Buffy coat, and Buffy coat vs. lysis, respectively [33]. In order to ensure the reliability for to clinical use of whole blood RNA diagnosis, the development of standard method and

The Gene Ontology (GO) Database was used to categorize gene expression profiles functionally to conduct the effects of white blood cells on whole blood gene expression profiles in our study of hyperlipidemia. As a result, the GO term, related to white blood cell function (GO: 0006954, 0007166), had a high correlation coefficient. In contrast, GO terms related to the repair of damaged organs, including translation (GO: 0006412), positive regulation of growth rate (GO: 0040010), and growth (GO: 004007), showed low correlation coefficients. We, therefore, conclude that the difference in the gene expression profiles between the whole blood and white blood cells are not only caused by differences in experimental protocols, but also by

Whole blood RNA is easy to handle compared to isolated white blood cell RNA and can be used for health and disease monitoring and animal control. In addition, whole blood is a heterogeneous mixture of subpopulation cells. Once a great change occurs in composition and expressing condition of subpopulations, their associated change will be reflected on

Whole blood microarray analyses were conducted to evaluate variations of correlation among individuals and ages using specific pathogen-free (SPF) Clawn miniature pigs. The characteristics of age-related gene expression by taking into account of change in the number of expressed genes by age and similarities of gene expression intensity between individuals were identified. As a result, the number of expressed genes was less in fetal stage and infancy period but increased with age, reaching a steady state of gene expression after 20 weeks of age. Variation in gene expression intensity within the same age was great in fetal stage and infancy period, but converged with age. The variation between 20 and 30

measurement standards needs to be sought.

differences in RNA origin [34].

**8. Conclusion** 

whole blood RNA.

The degree of stress can be comparable according to the numbers of up-regulated and down-regulated genes, even if the stress is different in quality from the others.

Sodium azide was given orally to the miniature pigs over 20 weeks. There were no significant changes of hematological and biochemical properties for administrated dose of 300µg/kg, one hundredth of LD50. On the other hand, gene expression profiles were obviously changed. Anesthesia group showed a slight degree, but the one week fasting group showed a significant difference. This can be clearly noticed when the contents of stress is classified by the function of up-regulated and down-regulated genes. Consequently, grade of the stress can be estimated according to the expression state of genes.


**Table 4.** Summery of gene expression condition of several types of stress Number of genes
