Classification of cDNA array genes that have a highly significant discriminative power due to their unique distribution in four brain regions

Gaining Confidence on Molecular Classification through Consensus Modeling and Validation

Current advances in genomics, proteomics, and metabonomics would result in a constellation of benefits in human health. Classification applying supervised learning methods to omics data as one of the molecular classification approaches has enjoyed its growing role in clinical application. However, the utility of a molecular classifier will not be fully appreciated unless its quality is carefully validated. A clinical omics data is usually noisy with the number of independent variables far more than the number of subjects and, possibly, with a skewed subject distribution. Given that, the consensus approach holds an advantage over a single classifier. Thus, the focus of this review is mainly placed on how validating a molecular classifier using Decision Forest (DF), a robust consensus approach. We recommended that a molecular classifier has to be assessed with respect to overall prediction accuracy, prediction confidence and chance correlation, which can be readily achieved in DF. The commonalities and differences between external validation and cross-validation are also discussed for perspective use of these methods to validate a DF classifier. In addition, the advantages of using consensus approaches for identification of potential biomarkers are also rationalized. Although specific DF examples are used in this review, the provided rationales and recommendations should be equally applicable to other consensus methods.

Identification of a set of genes showing regionally enriched expression in the mouse brain

Background

The Pleiades Promoter Project aims to improve gene therapy by designing human mini-promoters (< 4 kb) that drive gene expression in specific brain regions or cell-types of therapeutic interest. Our goal was to first identify genes displaying regionally enriched expression in the mouse brain so that promoters designed from orthologous human genes can then be tested to drive reporter expression in a similar pattern in the mouse brain.

Results

We have utilized LongSAGE to identify regionally enriched transcripts in the adult mouse brain. As supplemental strategies, we also performed a meta-analysis of published literature and inspected the Allen Brain Atlas in situ hybridization data. From a set of approximately 30,000 mouse genes, 237 were identified as showing specific or enriched expression in 30 target regions of the mouse brain. GO term over-representation among these genes revealed co-involvement in various aspects of central nervous system development and physiology.

Conclusion

Using a multi-faceted expression validation approach, we have identified mouse genes whose human orthologs are good candidates for design of mini-promoters. These mouse genes represent molecular markers in several discrete brain regions/cell-types, which could potentially provide a mechanistic explanation of unique functions performed by each region. This set of markers may also serve as a resource for further studies of gene regulatory elements influencing brain expression.

Gene expression profiles in rat brain disclose CNS signature genes and regional patterns of functional specialisation

Background

The mammalian brain is divided into distinct regions with structural and neurophysiological differences. As a result, gene expression is likely to vary between regions in relation to their cellular composition and neuronal function. In order to improve our knowledge and understanding of regional patterns of gene expression in the CNS, we have generated a global map of gene expression in selected regions of the adult rat brain (frontomedial-, temporal- and occipital cortex, hippocampus, striatum and cerebellum; both right and left sides) as well as in three major non-neural tissues (spleen, liver and kidney) using the Applied Biosystems Rat Genome Survey Microarray.

Results

By unsupervised hierarchical clustering, we found that the transcriptome within a region was highly conserved among individual rats and that there were no systematic differences between the two hemispheres (right versus left side). Further, we identified distinct sets of genes showing significant regional enrichment. Functional annotation of each of these gene sets clearly reflected several important physiological features of the region in question, including synaptic transmission within the cortex, neurogenesis in hippocampus and G-protein-mediated signalling in striatum. In addition, we were able to reveal potentially new regional features, such as mRNA transcription- and neurogenesis-annotated activities in cerebellum and differential use of glutamate signalling between regions. Finally, we determined a set of 'CNS-signature' genes that uncover characteristics of several common neuronal processes in the CNS, with marked over-representation of specific features of synaptic transmission, ion transport and cell communication, as well as numerous novel unclassified genes.

Conclusion

We have generated a global map of gene expression in the rat brain and used this to determine functional processes and pathways that have a regional preference or ubiquitous distribution within the CNS, respectively. The existence of shared specialised neuronal activities in CNS is interesting in a context of potential functional redundancy, and future studies should further explore the overall characteristics of CNS-specific versus region-specific gene profiles in the brain.