Gene expression changes with age in skin,adipose tissue,blood and brain

Background

Previous studies have demonstrated that gene expression levels change with age. These changes are hypothesized to influence the aging rate of an individual. We analyzed gene expression changes with age in abdominal skin, subcutaneous adipose tissue and lymphoblastoid cell lines in 856 female twins in the age range of 39-85 years. Additionally, we investigated genotypic variants involved in genotype-by-age interactions to understand how the genomic regulation of gene expression alters with age.

Results

Using a linear mixed model, differential expression with age was identified in 1,672 genes in skin and 188 genes in adipose tissue. Only two genes expressed in lymphoblastoid cell lines showed significant changes with age. Genes significantly regulated by age were compared with expression profiles in 10 brain regions from 100 postmortem brains aged 16 to 83 years. We identified only one age-related gene common to the three tissues. There were 12 genes that showed differential expression with age in both skin and brain tissue and three common to adipose and brain tissues.

Conclusions

Skin showed the most age-related gene expression changes of all the tissues investigated, with many of the genes being previously implicated in fatty acid metabolism, mitochondrial activity, cancer and splicing. A significant proportion of age-related changes in gene expression appear to be tissue-specific with only a few genes sharing an age effect in expression across tissues. More research is needed to improve our understanding of the genetic influences on aging and the relationship with age-related diseases.     

Average rank-based score to measure deregulation of molecular pathway gene sets

Background

Deregulation of biological pathways has been shown to be involved in the turmorigenesis of a variety of cancers. The co-regulation of pathways in tumor and normal tissues has not been studied in a systematic manner.

Results

In this study we propose a novel statistic named AR-score (average rank based score) to measure pathway activities based on microarray gene expression profiles. We calculate and compare the AR-scores of pathways in microarray datasets containing expression profiles for a wide range of cancer types as well as the corresponding normal tissues. We find that many pathways undergo significant activity changes in tumors with respect to normal tissues. AR-scores for a small subset of pathways are capable of distinguishing tumor from normal tissues or classifying tumor subtypes. In normal tissues many pathways are highly correlated in their activities, whereas their correlations reduce significantly in tumors and cancer cell lines. The co-expression of genes in the same pathways was also significantly perturbed in tumors.

Conclusions

The co-regulation of genes in the same pathways and co-regulation of different pathways are significantly perturbed in tumors versus normal tissues. Our method provides a useful tool for better understanding the mechanistic changes in tumors, which can also be used for exploring other biological problems.     

Clustered gene expression changes flank targeted gene loci in knockout mice

Background

Gene expression profiling using microarrays is a powerful technology widely used to study regulatory networks. Profiling of mRNA levels in mutant organisms has the potential to identify genes regulated by the mutated protein.

Methodology/Principle Findings

Using tissues from multiple lines of knockout mice we have examined genome-wide changes in gene expression. We report that a significant proportion of changed genes were found near the targeted gene.

Conclusions/Significance

The apparent clustering of these genes was explained by the presence of flanking DNA from the parental ES cell. We provide recommendations for the analysis and reporting of microarray data from knockout mice     

Gene Expression Analysis of Peripheral Cells for Subclassification of Pediatric Inflammatory Bowel Disease in Remission

Objective

In current clinical practice, optimal treatment of inflammatory bowel disease (IBD) aims at the induction and maintenance of clinical remission. Clinical remission is apparent when laboratory markers of inflammation are normal and clinical symptoms are absent. However, sub-clinical inflammation can still be present. A detailed analysis of the immune status during this inactive state of disease may provide a useful tool to categorize patients with clinical remission into subsets with variable states of immune activation.

Design

By using Affymetrix GeneChips, we analysed RNA gene expression profiles of peripheral blood leukocytes from pediatric IBD patients in clinical remission and controls. We performed (un)supervised clustering analysis of IBD-associated genes and applied Ingenuity® pathway software to identify specific molecular profiles between patients.

Results

Pediatric IBD patients with disease in clinical remission display heterogeneously distributed gene expression profiles that are significantly distinct from controls. We identified three clusters of IBD patients, each displaying specific expression profiles of IBD-associated genes.

Conclusion

The expression of immune- and IBD-associated genes in peripheral blood leukocytes from pediatric IBD patients in clinical remission was different from healthy controls, indicating that sub-clinical immune mechanisms are still active during remission. As such, RNA profiling of peripheral blood may allow for non-invasive patient subclassification and new perspectives in treatment regimes of IBD patients in the future.